M.E Computer Science Image Processing Projects

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List Link : http://kasanpro.com/projects-list/m-e-computer-science-image-processing-projects

Title :Texture Analysis and Classification with Linear Regression Model Based on Wavelet TransformLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/texture-analysis-classification-based-wavelet-transform

Abstract : The wevelet transform as an important multiresolution analysis tool has commonly applied to textureanalysis and classification. Nevertheless, it ignores the structural information while capturing the spectral informationof the texture image at different scales. In this paper, we propose a texture analysis and classification approach withthe linear regression model based on the wavelet transform. This method is motivated by the observation that thereexists a distinctive correlation between the sample images, belonging to the same kind of texture, at differentfrequency regions obtained by 2-D wavelet packet transform. Experimentally, it was observed that this correlationverious from texture to texture. The linear regression model is empolyed to analyze this correlation and extract texturefeature that characterize the samples. Therefore, our method considers not only the frequency regions but also thecorrelation betweem these regions. In contrast, the pyramid-structured wavelet transform (PSWT) and the tree-structured wavelet transform (TSWT) do not consider the correlation between different frequency regiond.Experiments show that our method significantly improves the texture classification rate in comparison with themultiresolution methods, including PSWT, TSWT, the Gabor transform, and some recently proposed methods dervedfrom these.

Title :Image Inpainting by Patch Propagation using Patch SparsityLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/image-inpainting-patch-propagation-patch-sparsity

Abstract : This paper introduces a novel examplar-based in-painting algorithm through investigating the sparsity ofnatural image patches. Two novel concepts of sparsity at the patch level are proposed for modeling the patch priorityand patch representation, which are two crucial steps for patch propagation in the examplar-based inpaintingapproach. First, patch structure sparsity is designed to measure the confidence of a patch located at the imagestructure (e.g., the edge or corner) by the sparseness of its nonzero similarities to the neighboring patches. The patchwith larger structure sparsity will be assigned higher priority for further inpainting. Second, it is assumed that the patchto be filled can be represented by the sparse linear combination of candidate patches under the local patchconsistency constraint in a framework of sparse representation. Compared with the traditional examplar-basedinpainting approach, structure sparsity enables better discrimination of structure and texture, and the patch sparserepresentation forces the newly inpainted regions to be sharp and consistent with the surrounding textures.Experiments on synthetic and natural images show the advantages of the proposed approach.

Title :Medical Image Fusion via an Effective Wavelet-Based ApproachLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/medical-image-fusion-effective-wavelet-based

Abstract : A novel wavelet-based approach for medical image fusion is presented, which is developed by taking intonot only account the characteristics of human visual system (HVS) but also the physical meaning of the waveletcoefficients. After the medical images to be fused are decomposed by the wavelet transform, different-fusion schemesfor combining the coefficients are proposed: coefficients in low-frequency band are selected with a visibility-basedscheme, and coefficients in high-frequency bands are selected with a variance based method. To overcome thepresence of noise and guarantee the homogeneity of the fused image, all the coefficients are subsequently performedby a window-based consistency verification process. The fused image is finally constructed by the inverse wavelettransform with all composite coefficients. To quantitatively evaluate and prove the performance of the proposedmethod, series of experiments and comparisons with some existing fusion methods are carried out in the paper.Experimental results on simulated and real medical images indicate that the proposed method is effective and can getsatisfactory fusion results.

Title :Face Recognition by Exploring Information Jointly in Space, Scale and OrientationLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/face-recognition-exploring-information-jointly-space-scale-orientation

Abstract : Information jointly contained in image space, scale and orientation domains can provide rich importantclues not seen in either individual of these domains. The position, spatial frequency and orientation selectivityproperties are believed to have an important role in visual perception. This paper proposes a novel facerepresentation and recognition approach by exploring information jointly in image space, scale and orientationdomains. Specifically, the face image is first decomposed into different scale and orientation responses by convolvingmultiscale and multior- ientation Gabor filters. Second, local binary pattern analysis is used to describe theneighboring relationship not only in image space, but also in different scale and orientation responses. This way,information from different domains is explored to give a good face representation for recognition. Discriminantclassification is then performed based upon weighted histogram intersection or conditional mutual information withlinear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databasesshow the significant advantages of the proposed method over the existing ones.

Title :Global Ridge Orientation Modeling for Partial Fingerprint IdentificationLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/global-ridge-orientation-modeling-partial-fingerprint-identification

Abstract : Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challengetoday. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, weinvestigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topologicalfeatures. Specifically, we propose an analytical approach for reconstructing the global topology representation from apartial fingerprint. Firstly, we present an inverse orientation model for describing the reconstruction problem. Then, weprovide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in theexisting segments while exploring missing structures in the unknown parts. We have further developed algorithms forestimating the missing orientation structures based on some a priori knowledge of ridge topology features. Ourstatistical experiments show that our proposed model-based approach can effectively reduce the number ofcandidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance forpartial fingerprint identification.

M.E Computer Science Image Processing Projects

Title :Energy-Efficient Localized Routing in Random Multihop Wireless NetworksLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/energy-efficient-localized-routing-random-multihop-wireless-networks

Abstract : A number of energy-aware routing protocols were proposed to seek the energy efficiency of routes inmultihop wireless networks. Among them, several geographical localized routing protocols were proposed to helpmaking smarter routing decision using only local information and reduce the routing overhead. However, all proposedlocalized routing methods cannot guarantee the energy efficiency of their routes. In this paper, we first give a simplelocalized routing algorithm, called Localized Energy-Aware Restricted Neighborhood routing (LEARN), which canguarantee the energy efficiency of its route if it can find the route successfully. We then theoretically study its criticaltransmission radius in random networks which can guarantee that LEARN routing finds a route for any source anddestination pairs asymptotically almost surely. We also extend the proposed routing into three-dimensional (3D)networks and derive its critical transmission radius in 3D random networks. Simulation results confirm our theoreticalanalysis of LEARN routing and demonstrate its energy efficiency in large scale random networks.

Title :Extraction of Head and Face Boundaries for Face Detection ApplicationLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/extraction-head-face-boundaries-face-detection-application

Abstract : Face detection is an importent first step to many advanced computer vision, biometrics and multimediaapplications such as face tracking, face recognition and video surveillance. In this paper, a faster face detectionsystem is proposed and the method of extracking head and face boundaries along with its facial features has beenutilized. Initially, boundary tracking is employed to extract the head and face boundaries from the image. Thisboundary tracking is done with the help of BW (Black and White) tracking function. Facial features are extracted using

gabor filter algorithm. The neural network employed for face detection is based on multi layer neurons architecturewhile is a feed forword network. This approch is even applicable for detecting faces in cluster images. Experimentalresults show that the proposed approach can perfrom the extraction human head, face boundaries and detection offace succesfully. The proposed technique can be applied for images with single face as well as nultiple faces and thefaces are detected succesfully with high detection rate when compared to the adaboost technique of face detection.

Title :Color Image Quantization Techique based on Image Compression for Power Consumption for EmbeddedSytemsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/color-image-quantization-techique-based-image-compression

Abstract : Data transmission over the Internet is prevalent and the development of efficient algorithms forcompressing such data in order to achieve reduced bandwidth has been an active research. With increased demandfor exchanges of datas over the Internet, research for data compression is more intense than ever before. Computingtechniques that would considerably reduce the number of colours in an image that occupies less space andbandwidth for transmission over networks form an active research. The less space and less bandwidth will alsoreduce the memory access for displaying image and this will lead to saving considerable amount of power in aresource constrained battery operated embedded system. In this project a new colour quantisation (CQ) technique isintroduced. The CQ technique is based on image split into sub-images and the use of self-organised neural networkclassifiers (SONNC). Initially, the dominant colours of each sub-image are extracted through SONNCs and then areused for the quantisation of the colours of the entire image. In addition, for the estimation of the proper number ofdominant image colours, a new algorithm based on the projection of the image colours into the first two principalcomponents is proposed. Applying a systematic design methodology to the developed CQ algorithm, an efficientembedded architecture based on the ARM7 processor achieving high-speed processing and less energyconsumption, is derived.

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews

Title :Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction ErrorLanguage : C#

Project Link : http://kasanpro.com/p/c-sharp/video-data-hiding-based-prediction-error

Abstract : This paper deals with data hiding in compressed video. Unlike data hiding in images and raw video whichoperates on the images themselves in the spatial or transformed domain which are vulnerable to steganalysis, wetarget the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional(B)-frames in compressed video. The choice of candidate subset of these motion vectors are based on theirassociated macro block prediction error, which is different from the approaches based on the motion vector attributessuch as the magnitude and phase angle, etc. A greedy adaptive threshold is searched for every frame to achieverobustness while maintaining a low prediction error level. The secret message bit stream is embedded in the leastsignificant bit of both components of the candidate motion vectors. The method is implemented and tested for hidingdata in natural sequences of multiple groups of pictures and the results are evaluated. The evaluation is based on twocriteria: minimum distortion to the reconstructed video and minimum overhead on the compressed video size. Basedon the aforementioned criteria, the proposed method is found to perform well and is compared to a motion vectorattribute-based method from the literature.

Title :A Medical Image Archive Solution in the CloudLanguage : C#

Project Link : http://kasanpro.com/p/c-sharp/medical-image-archive-solution-cloud

Abstract : Growing long-term cost of managing an onsite medical imaging archive has been a subject which thehealth care industry struggles with. Based on the current trend, it is estimated that over 1 billion diagnostic imagingprocedures will be performed in the United States during year 2014, generating about 100 Peta bytes of data. Thehigh volume of medical images is leading to scalability and maintenance issues with healthcare providers' onsitepicture archiving and communication system and network. Cloud computing promises lower cost, high scalability,availability and disaster recoverability which can be a natural solution some of the problems we faced for long-termmedical image archive. A prototype system was implemented to study such as solution on one of the industry leadingcloud computing platform, Microsoft Windows Azure. It includes a Digital Imaging and Communications in Medicine(DICOM) server which handles standard store/query/retrieve requests; a DICOM image indexer that parses themetadata and stores them in a SQL Azure database; and a web UI for searching and viewing archived images basedon patient and image attributes. The comprehensive tools and functionality of Windows Azure made it an ideal

platform to develop and deploy this kind of service oriented applications.

M.E Computer Science Image Processing Projects

Title :A Double Thresholding Method for Cancer Stem Cell DetectionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/double-thresholding-method-cancer-stem-cell-detection

Abstract : Image analysis of cancer cells is important for cancer diagnosis and therapy, because it recognized as themost efficient and effective way to observe its proliferation. For the purpose of adaptive and accurate cancer cellimage segmentation, a double threshold segmentation method is proposed in this paper. Based on a singlegray-value histogram of the RGB color space, a double threshold, the key parameters of threshold segmentationcomponent can be fixed histogram. As by a fitted-curve reasonable of thresholds the RGB confirmed, binarysegmentation dependent on two thresholds, will be put into practice and result in binary image. With thepost-processing of mathematical morphology and division of whole image, the better segmentation result can befinally achieved. By the comparison with other advanced segmentation methods such as level set and active contour,the proposed double thresholding has been found as the simplest strategy with shortest processing time as well ashighest accuracy. The proposed method can be effectively used in the detection and recognition of cancer stem cellsin images.

Title :The Automatic Detection Algorithm of Tongue Cancer Stem Cells Based on Fuzzy Pattern RecognitionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/detection-algorithm-tongue-cancer-stem-cells-based-fuzzy-pattern-recognition

Abstract : In this paper, we present a novel recognition algorithm for detecting tongue cancer stem cells with respectto appropriate scaling factors. Our method can be achieved by computer image processing in the condition that thecancer cells are undifferentiated or slightly differentiated, which is of important research significance in the realm oforal medicine. According to the biological natures of tongue cancer stem cells, we select the curvature variance of cellcontour, the nuclear- cytoplasmic area ratio, and the average optical density of cytoplasm as the measurementparameters. Using these three biological parameters, the characteristics of cancerous tumor cells can be describedand thus classified. Therefore, those cells can be categorized under the principle of maximum degree of membershipin fuzzy pattern recognition algorithms. In this way, the tongue cancer stem cells can be automatically detected.Desirable recognition results given by our experiments have substantiated the efficiency of our algorithm.

Title :Motion human detection based on background subtractionLanguage : C#

Project Link : http://kasanpro.com/p/c-sharp/motion-human-detection-based-background-subtraction

Abstract : According to the result of moving object detection research on video sequences, this paper proposes anew method to detect moving object based on background subtraction. First of all, we establish a reliable backgroundupdating model based on statistical and use a dynamic optimization threshold method to obtain a more completemoving object. And then, morphological filtering is introduced to eliminate the noise and solve the backgrounddisturbance problem. At last, contour projection analysis is combined with the shape analysis to remove the effect ofshadow, the moving human body are accurately and reliably detected. The experiment results show that the proposedmethod runs quickly, accurately and fits for the real-time detection.

Title :Image compression using image inpaintingLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/image-compression-image-inpainting

Abstract : This paper introduces a novel examplar-based inpainting algorithm through investigating the sparsity ofnatural image patches. Two novel concepts of sparsity at the patch level are proposed for modeling the patch priorityand patch representation, which are two crucial steps for patch propagation in the examplar-based inpaintingapproach. First, patch structure sparsity is designed to measure the confidence of a patch located at the imagestructure (e.g., the edge or corner) by the sparseness of its nonzero similarities to the neighboring patches. The patchwith larger structure sparsity will be assigned higher priority for further inpainting. Second, it is assumed that the patchto be filled can be represented by the sparse linear combination of candidate patches under the local patch

consistency constraint in a framework of sparse representation. Compared with the traditional examplar-basedinpainting approach, structure sparsity enables better discrimination of structure and texture, and the patch sparserepresentation forces the newly inpainted regions to be sharp and consistent with the surrounding textures.Experiments on synthetic and natural images show the advantages of the proposed approach.

Title :Brain Tumor Detection from Pre-Processed MR Images using Segmentation TechniquesLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/brain-tumor-detection-pre-processed-mr-images-segmentation

Abstract : Magnetic resonance imaging (MRI) has become a common way to study brain tumor. In this paper wepre-process the two-dimensional magnetic resonance images of brain and subsequently detect the tumor using edgedetection technique and color based segmentation algorithm. Edge-based segmentation has been implemented usingoperators e.g. Sobel, Prewitt, Canny and Laplacian of Gaussian operators. The color-based segmentation methodhas been accomplished using K-means clustering algorithm. The color-based segmentation carefully selects thetumor from the pre-processed image as a clustering feature. The present work demonstrates that the method cansuccessfully detect the brain tumor and thereby help the doctors for analyzing tumor size and region. The algorithmshave been developed on MATLAB version 7.6.0 (R2008a) platform.

M.E Computer Science Image Processing Projects

Title :Multiscale Modeling for Image Analysis of Brain Tumor StudiesLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/multiscale-modeling-image-analysis-brain-tumor-studies

Abstract : Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging.In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order toestablish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling incombination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a newmultiscale, multiphysics model including growth simulation from the cellular level up to the biomechanical level,accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerianapproach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, densecorrespondence between the modified atlas and patient image is established using nonrigid registration. The methodoffers opportunities in atlas-based segmentation of tumor- bearing brain images as well as for improvedpatient-specific simulation and prognosis of tumor progression.

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews

Title :Automatic Skin Lesion Segmentation via Iterative Stochastic Region MergingLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/automatic-skin-lesion-segmentation-iterative-stochastic-region-merging

Abstract : An automatic method for segmenting skin lesions in conventional macroscopic images is presented. Theimages are acquired with conventional cameras, without the use of a dermoscope. Automatic segmentation of skinlesions from macroscopic images is a very challenging problem due to factors such as illumination variations, irregularstructural and color variations, the presence of hair, as well as the occurrence of multiple unhealthy skin regions. Toaddress these factors, a novel iterative stochastic region-merging approach is employed to segment the regionscorresponding to skin lesions from the macroscopic images, where stochastic region merging is initialized first on apixel level, and subsequently on a region level until convergence. A region merging likelihood function based on theregional statistics is introduced to determine the merger of regions in a stochastic manner. Experimental results showthat the proposed system achieves overall segmentation error of under 10% for skin lesions in macroscopic images,which is lower than that achieved by existing methods.

Title :Adaptive Spectral Transform for Wavelet-Based Color Image CompressionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/adaptive-spectral-transform-wavelet-based-color-image-compression

Abstract : Since different regions of a color image generally exhibit different spectral characteristics, the energy

compaction of applying a single spectral transform to all regions is largely inefficient from a compression perspective.Thus, it is proposed that different subsets of wavelet coefficients of a color image be subjected to different spectraltransforms before the resultant coefficients are coded by an efficient wavelet coefficient coding scheme such as thatused in JPEG2000 or color set partitioning in hierarchical trees (CSPIHT). A quad tree represents the spatialpartitioning of the set of high frequency coefficients of the color planes into spatially oriented subsets which may befurther partitioned into smaller directionally oriented sub- sets. The partitioning decisions and decisions to employfixed or signal-dependent bases for each subset are rate-distortion (R-D) optimized by employing a known analyticalR-D model for these coefficient coding schemes. A compression system of asymmetric complexity, that integrates theproposed adaptive spectral transform with the CSPIHT coefficient coding scheme yields average coding gains of 0.3dB and 0.9 dB in the Y component at 1.0 b/p and 2.5 b/p, respectively, and 0.9 dB and 1.35 dB in the U and Vcomponents at 1.0 b/p and 2.5 b/p, respectively, over a reference compression system that integrates the singlespectral transform derived from the entire image with the CSPIHT coefficient coding scheme.

Title :Image Segmentation and Shape Analysis for Road-Sign DetectionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/image-segmentation-shape-analysis-road-sign-detection

Abstract : This paper proposes an automatic road-sign recognition method based on image segmentation and jointtransform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able todetect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular,triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, andocclusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing differentshapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond ornondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of thedistortion-invariant fringe-adjusted JTC (FJTC) technique for recognition. There are three main stages in the proposedalgorithm: 1) segmentation by clustering the pixels based on the color features to find the regions of interest (ROIs);2) traffic-sign detection by using two novel shape classification criteria, i.e., the relationship between area andperimeter and the number of sides of a given shape; and 3) recognition of the road sign using FJTC to match theunknown signs with the known reference road signs stored in the database. Experimental results on real-life imagesshow a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant totranslation, rotation, scale, and partial occlusions.

Title :Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words WeightingLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/bag-features-based-medical-image-retrieval-visual-words-weighting

Abstract : Bag-of-features based approaches have become prominent for image retrieval and image classificationtasks in the past decade. Such methods represent an image as a collection of local features, such as image patchesand key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, wefirst model the assignments of local descriptors as contribution functions, and then propose a novel multipleassignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in avocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to buildcontribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. Wefurther propose a novel visual word weighting method. The discriminative power of each visual word is analyzed bythe sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated asa weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. Theweighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical imageretrieval tasks. The methods are tested on three well-known data sets, i.e., the Image CLEFmed data set, the 304 CTSet, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignmentoutperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereasthe proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the termfrequency weights and the term frequency-inverse document frequency weights.

M.E Computer Science Image Processing Projects

Title :Bi-Level Image Compression Estimating the Markov Order of DependenciesLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/bi-level-image-compression-estimating-markov-order-dependencies

Abstract : This paper presents a bi-level image compression method based on chain codes and entropy coders.However, the proposed method also includes an order estimation process to estimate the order of dependencies that

may exist among the chain code symbols prior to the entropy coding stage. For each bi-level image, the method firstobtains its chain code representation and then estimates its order of symbol dependencies. This order value is usedto find the conditional and joint symbol probabilities corresponding to our newly defined Markov model. Our orderestimation process is based on the Bayesian information criterion (BIC), a statistically based model selectiontechnique that has proved to be a consistent order estimator. In our experiments, we show how our order estimationprocess can help achieve more efficient compression levels by providing comparisons against some of the mostcommonly used image compression standards such as the Graphics Interchange Format (GIF), Joint Bi-level ImageExperts Group (JBIG), and JBIG2.

Title :Supervised Spectral-Spatial Hyperspectral Image Classification With Weighted Markov Random FieldsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/supervised-spectral-spatial-hyperspectral-image-classification-with-weighted-markov-random-fields

Abstract : This paper presents a new approach for hyperspectral image classification exploiting spectral-spatialinformation. Under the maximum a posteriori framework, we propose a supervised classification model which includesa spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The datafidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, whilethe spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce aspatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixedas an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial andcontextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm,named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method ofmultipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperformsmany state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic.

Title :Reversible Image Data Hiding with Contrast EnhancementLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/reversible-image-data-hiding-contrast-enhancement

Abstract : In this letter, a novel reversible data hiding (RDH) algorithm is proposed for digital images. Instead of tryingto keep the PSNR value high, the proposed algorithm enhances the contrast of a host image to improve its visualquality. The highest two bins in the histogram are selected for data embedding so that histogram equalization can beperformed by repeating the process. The side information is embedded along with the message bits into the hostimage so that the original image is completely recoverable. The proposed algorithm was implemented on two sets ofimages to demonstrate its efficiency. To our best knowledge, it is the first algorithm that achieves image contrastenhancement byRDH. Furthermore, the evaluation results show that the visual quality can be preserved after aconsiderable amount of message bits have been embedded into the contrast-enhanced images, even better thanthree specificMATLAB functions used for image contrast enhancement.

Title :An Efficient MRF Embedded Level Set Method for Image SegmentationLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/mrf-embedded-level-set-method-image-segmentation

Abstract : This paper presents a fast and robust level set method for image segmentation. To enhance therobustness against noise, we embed a Markov random field (MRF) energy function to the conventional level setenergy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them tofall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraicmultigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain,respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of ourmethod for big image databases. By comparing the proposed fast and robust level set method with the standard levelset method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medicalimages and natural images, we comprehensively demonstrate the new method is robust against various kinds ofnoises. Especially, the new level set method can segment an image of size 500 by 500 within three seconds onMATLAB R2010b installed in a computer with 3.30GHz CPU and 4GB memory.

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews

Title :Image Sensor-Based Heart Rate Evaluation From Face Reflectance Using Hilbert-Huang TransformLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/heart-rate-evaluation-from-face-reflectance-using-hilbert-huang-transform

Abstract : Monitoring heart rates using conventional electrocardiogram equipment requires patients to wear adhesivegel patches or chest straps that can cause skin irritation and discomfort. Commercially available pulse oximetrysensors that attach to the fingertips or earlobes also cause inconvenience for patients and the spring-loaded clips canbe painful to use. Therefore, a novel robust face-based heart rate monitoring technique is proposed to allow for theevaluation of heart rate variation without physical contact with the patient. Face reflectance is first decomposed from asingle image and then heart rate evaluation is conducted from consecutive frames according to the periodic variationof reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum asheartbeats cause changes to blood volume in the blood vessels in the face. To achieve a robust evaluation, ensembleempirical mode decomposition of the Hilbert-Huang transform is used to acquire the primary heart rate signal whilereducing the effect of ambient light changes. Our proposed approach is found to outperform the current state of theart, providing greater measurement accuracy with smaller variance and is shown to be feasible in real-worldenvironments.

M.E Computer Science Image Processing Projects

Title :Fusion-Based Restoration of The Underwater ImagesLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/fusion-based-restoration-the-underwater-images

Abstract : In this paper we introduce a novel strategy that effectively enhance the visibility of underwater images. Ourmethod is build-up on the fusion strategy that takes a sequence of inputs derived from the initial image. Practically,our fusion-based method aims to yield a final image that overcomes the deficiencies existing in the degraded inputimages by employing several weight maps that discriminate the regions characterized by poor visibility. The extensiveexperiments demonstrate the utility of our solution since the visibility range of the underwater images is significantlyincreased by improving both the scene contrast and the color appearance.

Title :Image Registration By Region Cluster SIFT MatchingLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/image-registration-by-region-cluster-sift-matching

Abstract : Multi sensor image registration becoming a challenge task due to the poor performance of there sensitivityin scale, intensity variation and distortion. In this paper an optimized region cluster SIFT technique is used to imageregistration. This technique has five phases. In the first phase Scale Invariant Feature Transform (SIFT) is applied toextract key points in the referenced image. In the second phase, reference image segmented by regions by colorbased segmentation approach, these are called clusters in the reference image. In the third phase difference ofGaussian (DoG) filter is applied and key points with low contrast, localed at edge are discarderd. The fourth phase isthe matching phase, to achieve the distortion invariant or resolution invariant registration, key points are matchedaccording to the clusters in both referenced image and target image. Finally the fourth phase is the piece wisetransformation is applied to set the resultant image.

Title :Image Registration By Maximal Planar GraphLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/image-registration-by-maximal-planar-graph

Abstract : Multi sensor image registration becoming a challenge task due to the poor performance of think sensitivityin scale, intensity variation and distortion. In this paper SIFT technique is used to image registration. This techniquehas five phases. In the first phase Scale Invariant Feature Transform (SIFT) is applied to extract key points in thereferenced image. In the second phase, reference image segmented by regions by color segmentation approach, Inthe third phase an maximal planer graph is constructed by region adjacency. In the fourth step loaded key points arere ducted by comparing with maximal element graph lines in points with distance less than a three old with thenearest graph edge are included and other points are discarded. Finally the fourth phase is the piece wisetransformation is applied to set the resultant image.

Title :Image Segmentation and Shape Analysis for Road-Sign DetectionLanguage : C#

Project Link : http://kasanpro.com/p/c-sharp/image-segmentation-shape-analysis-road-sign-detection-code

Abstract : This paper proposes an automatic road-sign recognition method based on image segmentation and jointtransform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able todetect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular,triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, andocclusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing differentshapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond ornondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of thedistortion-invariant fringe-adjusted JTC (FJTC) technique for recognition. There are three main stages in the proposedalgorithm: 1) segmentation by clustering the pixels based on the color features to find the regions of interest (ROIs);2) traffic-sign detection by using two novel shape classification criteria, i.e., the relationship between area andperimeter and the number of sides of a given shape; and 3) recognition of the road sign using FJTC to match theunknown signs with the known reference road signs stored in the database. Experimental results on real-life imagesshow a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant totranslation, rotation, scale, and partial occlusions.

Title :Automatic Image Registration using SIFT-NCCLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/automatic-image-registration-sift-ncc

Abstract : Accurate, robust and automatic image registration is critical task in many typical applications that employmulti-sensor and/or multi-date imagery information. The main content of this paper is an algorithm for the registrationof digital images. Some multi-sensed or temporal images contain large number of speckles and noise, or image canhave some distortion by some means. For these reasons, we need to remove the noises, speckle and to recover fromdistortion. We register two to find the similarity between the images. This paper discusses techniques for imageregistration based on SIFT. In this proposed framework we use NCC metrics for optimizing the matching work. Bestbin first search using kd tree is used for feature matching and RANSAC is used for outlier elimination.

M.E Computer Science Image Processing Projects

Title :Outdoor Scene Image Segmentation Based on Background Recognition and Perceptual OrganizationLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/outdoor-scene-image-segmentation-based-background-recognition

Abstract : In this paper, we propose a novel outdoor scene image segmentation algorithm based on backgroundrecognition and perceptual organization. We recognize the background objects such as the sky, the ground, andvegetation based on the color and texture information. For the structurally challenging objects, which usually consistof multiple constituent parts, we developed a perceptual organization model that can capture the nonacci- dentalstructural relationships among the constituent parts of the structured objects and, hence, group them togetheraccordingly without depending on a priori knowledge of the specific objects. Our experimental results show that ourproposed method outper- formed two state-of-the-art image segmentation approaches on two challenging outdoordatabases (Gould data set and Berkeley segmentation data set) and achieved accurate segmentation quality onvarious outdoor natural scene environments.

Title :Optimal Design of a Tilling Machine Reduction Gearbox Using MatlabLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/tilling-machine-reduction-gearbox

Abstract : This paper describes the optimal design of the reduction gearbox of a tillage machine. The minimumcenter diameter was selected as the objective, and the contact fatigue strength, bending fatigue strength, condition ofnonintervention, and oil film thickness ratio of the gearbox were applied as constraint conditions. The optimal modelwas solved by a Matlab program. The results show that the center diameter of the reduction gearbox decreased byabout 10%. The resulting decrease in weight and volume led to a reduction in the amount of gearbox material and aconsequent decrease in production cost.

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews

Title :Multimodal Analysis for Identification and Segmentation of Moving-Sounding Objects

Language : Matlab

Project Link : http://kasanpro.com/p/matlab/multimodal-analysis-identification-segmentation-moving-sounding-objects

Abstract : In this paper, we propose a novel method that exploits correlation between audio-visual dynamics of avideo to segment and localize objects that are the dominant source of audio. Our approach consists of a two-stepspatiotemporal segmentation mechanism that relies on velocity and acceleration of moving objects as visual features.Each frame of the video is segmented into regions based on motion and appearance cues using the QuickShiftalgorithm, which are then clustered over time using K-means, so as to obtain a spatiotemporal video segmentation.The video is represented by motion features computed over individual segments. The Mel-Frequency CepstralCoefficients (MFCC) of the audio signal, and their first order derivatives are exploited to represent audio. Theproposed framework assumes there is a non-trivial correlation between these audio features and the velocity andacceleration of the moving and sounding objects. The canonical correlation analysis (CCA) is utilized to identify themoving objects which are most correlated to the audio signal. In addition to moving-sounding object identification, thesame framework is also exploited to solve the problem of audio-video synchronization, and is used to aid interactivesegmentation. We evaluate the performance of our proposed method on challenging videos. Our experimentsdemonstrate significant increase in performance over the state-of-the-art both qualitatively and quantitatively, andvalidate the feasibility and superiority of our approach.

Title :Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel SelectionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/efficient-reversible-watermarking-based-adaptive-prediction-error-expansion

Abstract : Prediction-error expansion (PEE) is an important technique of reversible watermarking which can embedlarge payloads into digital images with low distortion. In this paper, the PEE technique is further investigated and anefficient reversible watermarking scheme is proposed, by incorporating in PEE two new strategies, namely, adaptiveembedding and pixel selection. Unlike conventional PEE which embeds data uniformly, we propose to adaptivelyembed 1 or 2 bits into expandable pixel ac- cording to the local complexity. This avoids expanding pixels with largeprediction-errors, and thus, it reduces embedding impact by decreasing the maximum modification to pixel values.Meanwhile, adaptive PEE allows very large payload in a single embedding pass, and it improves the capacity limit ofconventional PEE. We also propose to select pixels of smooth area for data embedding and leave rough pixelsunchanged. In this way, compared with conventional PEE, a more sharply distributed prediction-error histogram isobtained and a better visual quality of watermarked image is observed. With these improvements, our method outper-forms conventional PEE. Its superiority over other state-of-the-art methods is also demonstrated experimentally.

Title :Efficient Generalized Integer Transform for Reversible WatermarkingLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/efficient-generalized-integer-transform-reversible-watermarking

Abstract : In this letter, an efficient integer transform based reversible watermarking is proposed. We first show thatTian's difference expansion (DE) technique can be reformulated as an integer transform. Then, a generalized integertransform and a payload-dependent location map are constructed to extend the DE technique to the pixel blocks ofarbitrary length. Meanwhile, the distortion can be controlled by preferentially selecting embeddable blocks thatintroduce less distortion. Finally, the superiority of the proposed method is experimental verified by comparing withother existing schemes.

M.E Computer Science Image Processing Projects

Title :Reversible Image Watermarking Using Interpolation TechniqueLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/reversible-image-watermarking-using-interpolation-technique

Abstract : Watermarking embeds information into a digital signal like audio, image, or video. Reversible imagewatermarking can restore the original image without any distortion after the hidden data is extracted. In this paper, wepresent a novel reversible watermarking scheme using an interpolation technique, which can embed a large amountof covert data into images with imperceptible modification. Different from previous watermarking schemes, we utilizethe interpolation-error, the difference between interpolation value and corresponding pixel value, to embed bit "1" or"0" by expanding it additively or leaving it unchanged. Due to the slight modification of pixels, high image quality is

preserved. Exper- imental results also demonstrate that the proposed scheme can provide greater payload capacityand higher image fidelity compared with other state-of-the-art schemes.

Title :License Plate Character Recognition System using Neural NetworkLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/license-plate-character-recognition-system-neural-network

Abstract : Intelligent Transportation System (ITS) has become an integral part of the Transportation Industry thesedays and it consists of License Plate Recognition (LPR) System. License Plate Recognition is also called Car PlateRecognition (CPR) or Automatic Number Plate Recognition (ANPR) System. In LPR System, when a vehicle stepsover magnetic loop detector it senses car and takes image of the car, following image preprocessing operations forimprovement in the quality of car image. From this enhanced image, license plate region is recognized and extracted.Then character fragmentation/segmentation is performed on extracted License Plate and these segmented charactersare recognized using Neural Network in this paper.

Title :License Plate Recognition System using Visual WordsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/license-plate-recognition-system-visual-words

Abstract :

Title :Reconstruction of Underwater Image by BispectrumLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/reconstruction-underwater-image-bispectrum

Abstract : Reconstruction of an underwater object from a sequence of images distorted by moving water waves is achallenging task. A new approach is presented in this paper. We make use of the bispectrum technique to analyze theraw image sequences and recover the phase information of the true object. We test our approach on both simulatedand real-world data, sepa- rately. Results show that our algorithm is very promising. Such technique has wideapplications to areas such as ocean study and submarine observation.

Title :Visually Lossless Encoding for JPEG2000Language : Matlab

Project Link : http://kasanpro.com/p/matlab/visually-lossless-encoding-jpeg2001

Abstract : Due to exponential growth in image sizes, visually lossless coding is increasingly considered as analternative to numerically lossless coding, which has limited compression ratios. This paper presents a method ofencoding color images in a visually lossless manner using JPEG2000. In order to hide coding artifacts caused byquantization, visibility thresholds (VTs) are measured and used for quantization of subbands in JPEG2000. The VTsare experimentally determined from statistically mod- eled quantization distortion, which is based on the distribution ofwavelet coefficients and the dead-zone quantizer of JPEG2000. The resulting VTs are adjusted for locally changingbackgrounds through a visual masking model, and then used to determine the minimum number of coding passes tobe included in the final codestream for visually lossless quality under the desired viewing conditions. Codestreamsproduced by this scheme are fully JPEG2000 Part-I compliant.

M.E Computer Science Image Processing Projects

Title :2D Image Morphing using Pixels based Color Transition MethodsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/morphing-2d-image-pixels-based-color-transition-me...

Abstract : Image morphing is the construction of an image sequence depicting a gradual transition between twoimages, has been extensively investigated now a days. 2D image morphing adds animations to the silent photographswhich generally communicate limited information. The color transition method used in image morphing decides thequality of the intermediate images generated by controlling the color blending rate. If the color blending is done

uniformly throughout the morphing process, good morph sequence is generated. Morph sequence has earlier morphssimilar to source and last morphs similar to the target image. The middle image in the entire morph sequence isneither source nor the target image. Hence the quality of morphs depends on the quality of middle images. If it lookgood then entire sequence looks good. In this paper methods of color transition by averaging the pixels and bymerging the color difference between pixels are proposed. The later one generates better quality middle image andentire morph sequence than most commonly used cross dissolve method of color transition.

Title :2D Image Morphing using Pixels based Color Transition MethodsLanguage : C#

Project Link : http://kasanpro.com/p/c-sharp/morphing-2d-image-pixels-based-color-transition

Abstract : Image morphing is the construction of an image sequence depicting a gradual transition between twoimages, has been extensively investigated now a days. 2D image morphing adds animations to the silent photographswhich generally communicate limited information. The color transition method used in image morphing decides thequality of the intermediate images generated by controlling the color blending rate. If the color blending is doneuniformly throughout the morphing process, good morph sequence is generated. Morph sequence has earlier morphssimilar to source and last morphs similar to the target image. The middle image in the entire morph sequence isneither source nor the target image. Hence the quality of morphs depends on the quality of middle images. If it lookgood then entire sequence looks good. In this paper methods of color transition by averaging the pixels and bymerging the color difference between pixels are proposed. The later one generates better quality middle image andentire morph sequence than most commonly used cross dissolve method of color transition.

Title :Underwater Image Enhancement based on Wavelet DecompositionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/underwater-image-enhancement-based-wavelet-decomposition

Abstract :

Title :An Enhanced Bag of Visual Word Vector Space Model to Represent Visual Content in Athletics ImagesLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/bag-visual-word-vector-space-model-visual-content-athletics-images

Abstract : Images that have a different visual appearance may be semantically related using a higher levelconceptualization. However, image classification and retrieval systems tend to rely only on the low-level visualstructure within images. This paper presents a framework to deal with this semantic gap limitation by exploiting thewell-known bag-of-visual words (BVW) to represent visual content. The novelty of this paper is threefold. First, thequality of visual words is improved by constructing visual words from representative keypoints. Second, domainspecific 'non-informative visual words' are detected which are useless to represent the content of visual data butwhich can degrade the categorization capability. Distinct from existing frameworks, two main characteristics fornon-informative visual words are defined: a high document frequency (DF) and a small statistical association with allthe concepts in the collection. The third contribution in this paper is that a novel method is used to restructure thevector space model of visual words with respect to a structural ontology model in order to resolve visual synonym andpolysemy problems. The experimental results show that our method can disambiguate visual word senses effectivelyand can significantly improve classification, interpretation, and retrieval performance for the athletics images.

Title :A New Approach to Image Compression Using Vector Quantization of Wavelet CoefficientsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/image-compression-using-vector-quantization-wavelet-coefficients

Abstract : Traditional image coding methods, such as vector quantization (VQ), discrete cosine transform (DCT)based coding, and entropy coding of subband, have been designed to eliminate statistical redundancy within stillimages. In this paper, a combined approach utilizing both transform coding and vector quantization techniques isused, hoping to achieve the best result in terms of compression ratio with acceptable recovery quality. The transformcoding used is 2-D wavelet transform and the key is to tap the correlation between wavelet coefficients of differentsubbands in the same spatial location rather than only in the same orientation. Performance comparisons are madewith three other VQ-based compression models. The result shows the strength of this novel approach in that it hasthe best reconstructed image quality in terms of its signal to noise ratio for a fixed compression ratio.

M.E Computer Science Image Processing Projects

Title :FABRIC DEFECT DETECTION USING MULTI-LEVEL TUNED-MATCHED GABOR FILTERSLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/fabric-defect-detection-using-multi-level-tuned-matched-gabor-filters

Abstract : This paper proposes a new defect detection scheme for woven fabrics. The proposed scheme is dividedinto two parts, namely the training part and the defect detection part. In the training part, a non-defective fabric imageis used as a template image, and a finite set of multi-level Gabor wavelets are tuned to match the texture informationof the image. In the defect detection part, filtered images from different levels are fused together and the constructeddetection scheme is used to detect defects in fabric sample images with the same texture background as that of thetemplate image. A filter selection method is also developed to select optimal filters to facilitate defect detection. Thenovelty of the method comes from the observation that a Gabor filter with finer resolutions than the fabric defects yarncan contribute very little for defect segmentation but need additional computational time. The proposed scheme istested by using 78 homogeneous textile fabric images. The results exhibit ac- curate defect detections with lowerfalse alarms than using the standard Gabor wavelets. Analysis of the computational complexity of the proposeddetection scheme is derived, which shows that the scheme can be implemented in real time easily.

Title :Video Watermarking Scheme Based on Principal Component Analysis and Wavelet TransformLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/video-watermarking-scheme-based-principal-component-analysis-wavelet-transform

Abstract : This paper presents a novel technique for embedding a binary logo watermark into video frames. Theproposed scheme is an imperceptible and a robust hybrid video watermarking scheme. PCA is applied to each blockof the two bands (LL - HH) which result from Discrete Wavelet transform of every video frame. The watermark isembedded into the principal components of the LL blocks and HH blocks in different ways. Combining the twotransforms improved the performance of the watermark algorithm. The scheme is tested by applying various attacks.Experimental results show no visible difference between the watermarked frames and the original frames and showthe robustness against a wide range of attacks such as MPEG coding, JPEG coding, Gaussian noise addition,histogram equalization, gamma correction, contrast adjustment, sharpen filter, cropping, resizing, and rotation.

Title :Super-Resolution-based InpaintingLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/super-resolution-based-inpainting

Abstract : This paper introduces a new examplar-based inpainting frame- work. A coarse version of the input imageis first inpainted by a non- parametric patch sampling. Compared to existing approaches, some im-provements havebeen done (e.g. filling order computation, combination of K nearest neighbours). The inpainted of a coarse version ofthe input image allows to reduce the computational complexity, to be less sensitive to noise and to work with thedominant orientations of image structures. From the low-resolution inpainted image, a single-image super-resolutionis applied to recover the details of missing areas. Experimental results on natural images and texture synthesisdemonstrate the effectiveness of the proposed method .

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews

Title :Scalable Face Image Retrieval using Attribute-Enhanced Sparse CodewordsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/scalable-face-image-retrieval-attribute-enhanced-sparsewords

Abstract : Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with theexponentially growing photos, large-scale content-based face image retrieval is an enabling technology for manyemerging applications. In this work, we aim to utilize automatically detected human attributes that contain semanticcues of the face photos to improve content- based face retrieval by constructing semantic codewords for effi- cientlarge-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose twoorthogonal methods named attribute-enhanced sparse coding and attribute- embedded inverted indexing to improvethe face retrieval in the offline and online stages. We investigate the effectiveness of different attributes and vitalfactors essential for face retrieval. Experimenting on two public datasets, the results show that the proposed methods

can achieve up to 43.5% relative improvement in MAP compared to the existing methods.

Title :Multichannel Non-Local Means Fusion for Color Image DenoisingLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/multichannel-non-local-means-fusion-color-image-denoising

Abstract : In this paper, we propose an advanced color image denoising scheme called multichannel non-localmeans fusion (MNLF), where noise reduction is formulated as the minimization of a penalty function. An inherentfeature of color images is the strong inter-channel correlation, which is introduced into the penalty function asadditional prior constraints to expect a better performance. The optimal solution of the minimization problem is derivedconsisting of constructing and fusing multiple non- local means (NLM) spanning all three channels. The weights in thefusion are optimized to minimize the overall mean squared denoising error, with the help of the extended and adaptedStein's unbiased risk estimator (SURE). Simulations on representative test images under various noise levels verifythe improvement brought by the multichannel NLM compared to the traditional single-channel NLM. Meanwhile,MNLF provides competitive performance both in terms of the color peak signal-to-noise ratio (cPSNR) and inperceptual quality when compared with other state-of-the-art benchmarks.

M.E Computer Science Image Processing Projects

Title :Noise Reduction Based on Partial-Reference, Dual-Tree Complex Wavelet Transform ShrinkageLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/noise-reduction-based-partial-reference-dual-tree-complex-wavelet-transform-shrinkage

Abstract : This paper presents a novel way to reduce noise introduced or exacerbated by image enhancementmethods, in particular algorithms based on the random spray sampling technique, but not only. According to thenature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. Toavoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, thenon-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise. Takingadvantage of the higher sensitivity of the human visual system to changes in brightness, the analysis can be limited tothe luma channel of both the non-enhanced and enhanced image. Also, given the importance of directional content inhuman vision, the analysis is performed through the dual-tree complex wavelet transform (DTWCT). Unlike thediscrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space. For eachlevel of the transform, the standard deviation of the non-enhanced image coefficients is computed across the sixorientations of the DTWCT, then it is normalized. The result is a map of the directional structures present in thenon-enhanced image. Said map is then used to shrink the coefficients of the enhanced image. The shrunk coefficientsand the coefficients from the non-enhanced image are then mixed according to data directionality. Finally, anoise-reduced version of the enhanced image is computed via the inverse transforms. A thorough numerical analysisof the results has been performed in order to confirm the validity of the proposed approach.

Title :Hyperspectral image noise reduction based on rank-1 tensor decompositionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/hyperspectral-image-noise-reduction-based-rank-1-tensor-decomposition

Abstract : In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based onhigh-order rank-1 tensor decomposition. The hyperspectral data cube is considered as a three-order tensor that isable to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD)algorithm is applied to the tensor data, which takes into account both the spatial and spectral information of thehyperspectral data cube. A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensorsusing an eigenvalue intensity sorting and reconstruction technique. Compared with the existing noise reductionmethods such as the conventional channel-by-channel approaches and the recently developed multidimensional filter,the spatial-spectral adaptive total variation filter, experiments with both synthetic noisy data and real HSI data revealthat the proposed R1TD algorithm significantly improves the HSI data quality in terms of both visual inspection andimage quality indices. The subsequent image classification results further validate the effectiveness of the pro- posedHSI noise reduction algorithm.

Title :Fuzzy C-Means Clustering with Local Information and Kernel Metric for Image SegmentationLanguage : Matlab

Project Link :

http://kasanpro.com/p/matlab/fuzzy-c-means-clustering-local-information-kernel-metric-image-segmentation

Abstract : In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation byintroducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on thespace distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the newalgorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustnessto noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptivelydetermines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all datapoints in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are bothparameter free. Experimental results on synthetic and real images show that the new algorithm is effective andefficient, and is relatively independent of this type of noise.

Title :Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction ErrorLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/data-hiding-motion-vectors-noise-video-based-their-associated-prediction-error

Abstract : This paper deals with data hiding in compressed video. Unlike data hiding in images and raw video whichoperates on the images themselves in the spatial or transformed domain which are vulnerable to steganalysis, wetarget the motion vectors used to encode and reconstruct both the forward predictive (P)-frame and bidirectional(B)-frames in compressed video. The choice of candidate subset of these motion vectors are based on theirassociated macro block prediction error, which is different from the approaches based on the motion vector attributessuch as the magnitude and phase angle, etc. A greedy adaptive threshold is searched for every frame to achieverobustness while maintaining a low prediction error level. The secret message bit stream is embedded in the leastsignificant bit of both components of the candidate motion vectors. The method is implemented and tested for hidingdata in natural sequences of multiple groups of pictures and the results are evaluated. The evaluation is based on twocriteria: minimum distortion to the reconstructed video and minimum overhead on the compressed video size. Basedon the aforementioned criteria, the proposed method is found to perform well and is compared to a motion vectorattribute-based method from the literature.

Title :Hyperspectral Image Noise Reduction based on K-SVD Tensor DecompositionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/hyperspectral-image-noise-reduction-based-k-svd-tensor-decomposition

Abstract : In this Paper,mixed noise reduction algorithm for hyperspectral imagery (HSI). The hyperspectral datacube is considered as a three-order tensor that is able to jointly treat both the spatial and spectral modes.This entiredenoising process is based on the K-SVD denoising algorithm.Our work involved in minimization model to removemixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the HSI data. Tosolve the weighted rank-one approximation problem arisen from the proposed model, a new iterative scheme is givenand the low rank approximation can be obtained by singular value decomposition (SVD, which takes into account boththe spatial and spectral information of the hyperspectral data cube and we present a new weighting data fidelityfunction, which has the same minimizer as the original likelihood functional but is much easier to optimize. Theweighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection interms of the different estimated noise parameters.

M.E Computer Science Image Processing Projects

Title :Structural Texture Similarity Metrics for Image Analysis and RetrievalLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/structural-texture-similarity-metrics-image-analysis-retrieval

Abstract : We develop new metrics for texture similarity that accounts for human visual perception and the stochasticnature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviationsbetween textures that according to human judgment are essentially identical. The proposed metrics extend the ideasof structural similarity and are guided by research in texture analysis-synthesis. They are implemented using asteerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in slidingwindows. We conduct systematic tests to investigate metric performance in the context of "known-item search," theretrieval of textures that are "identical" to the query texture. This eliminates the need for cumbersome subjective tests,thus enabling comparisons with human performance on a large database. Our experimental results indicate that theproposed metrics outperform peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM) and its variations,

as well as state-of- the-art texture classification metrics, using standard statistical measures.

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews

Title :Identification and Segmentation of Moving-Sounding Objects using Background SubstractionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/identification-segmentation-moving-sounding-objects-background-substraction

Abstract : In this paper, we propose a novel method for segmentation of moving and sounding objects byinvestigating the maximum correlation between the audio and visual features. This paper presents a new algorithm fordetecting moving objects from a static background scene to detect moving object based on background subtraction.We set up a reliable background updating model based on statistical. We use this motion regions as visual featureswhich are then grouped together, and for audio features we use MFCC and the first derivative of MFCC (MFCC_D) .We assume that the velocity of objects is correlated to the MFCC features, while their acceleration is correlated toMFCC_D. The maximum correlation is computed using canonical correlation (CCA) which is a method for finding themaximum correlation between two random variables with different dimensionality.CCA can be considered as aneigensystem problem. For an eigensystem to have a solution, enough samples are needed to estimate the statisticsof the signals. Since the correlation is usually analyzed over a small number of frames (i.e., number of samples), wepropose to represent audio and visual modalities at a higher level of abstraction. Video motion analysis concerns thedetection, tracking and recognition of moving behaviors, from image sequences. According to the result of movingobject detection research on video sequences.

Title :Building Change Detection Based on Satellite Stereo Imagery and Digital Surface ModelsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/building-change-detection-based-satellite-stereo-imagery-digital-surface-model

Abstract : Building change detection is a major issue for urban area monitoring. Due to different imaging conditionsand sensor parameters, 2-D information delivered by satellite images from different dates is often not sufficient whendealing with building changes. Moreover, due to the similar spectral characteristics, it is often difficult to distinguishbuildings from other man-made constructions, like roads and bridges, during the change detection procedure.Therefore, stereo imagery is of importance to provide the height component which is very helpful in analyzing 3-Dbuilding changes. In this paper, we propose a change detection method based on stereo imagery and digital surfacemodels (DSMs) generated with stereo matching methodology and provide a solution by the joint use of heightchanges and Kullback-Leibler divergence similarity measure between the original images. The Dempster-Shaferfusion theory is adopted to combine these two change indicators to improve the accuracy. In addition, vegetation andshadow classifications are used as no-building change indicators for refining the change detection results. In the end,an object-based building extraction method based on shape features is performed. For evaluation purpose, theproposed method is applied in two test areas, one is in an industrial area in Korea with stereo imagery from the samesensor and the other represents a dense urban area in Germany using stereo imagery from different sensors withdifferent resolutions. Our experimental results confirm the efficiency and high accuracy of the proposed methodologyeven for different kinds and combinations of stereo images and consequently different DSM qualities.

Title :PCA Feature Extraction for Change Detection in Multidimensional Unlabelled DataLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/pca-feature-extraction-change-detection-multidimensional-unlabelled-data

Abstract : When classifiers are deployed in real world applications, it is assumed that the distribution of the incomingdata matches the distribution of the data used to train the classifier. This assumption is often incorrect, whichnecessitates some form of change detection or adaptive classification. While there is a lot of research on changedetection based on the classification error, monitored over the course of the operation of the classifier, findingchanges in multidimensional unlabelled data is still a challenge. Here we propose to apply principal componentanalysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue thatthe components with the lowest variance should be retained as the extracted features because they are more likely tobe affected by a change. We chose a recently proposed semi-parametric log-likelihood change detection criterion(SPLL) which is sensitive to changes in both mean and variance of the multidimensional distribution. An experimentwith 35 data sets and an illustration with a simple video segmentation demonstrate the advantage of using extractedfeatures compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically

for data with multiple balanced classes.

Title :Remote Sensing Image Segmentation by Combining Spectral and Texture FeaturesLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/remote-sensing-image-segmentation-combining-spectral-texture-features

Abstract : We present a new method for remote sensing image segmentation, which utilizes both spectral andtexture information. Linear filters are used to provide enhanced spatial patterns. For each pixel location, we computecombined spectral and texture features using local spectral histograms, which concatenate local histograms of allinput bands. We regard each feature as a linear combination of several representative features, each of whichcorresponds to a segment. Segmentation is given by estimating combination weights, which indicate segmentownership of pixels. We present segmentation solutions where representative features are either known or unknown.We also show that feature dimensions can be greatly reduced via subspace projection. The scale issue isinvestigated, and an algorithm is presented to automatically select proper scales, which does not requiresegmentation at multiplescale levels. Experimental results demonstrate the promise of the proposed method.

M.E Computer Science Image Processing Projects

Title :Garment Personalization via Identity TransferLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/garment-personalization-identity-transfer

Abstract : We aim to create a more precise, natural clothing fit for users. We concentrate on a single image, strivingfor high-quality results that create the experience of an identity transfer. The input to our system comprises a pictureof the system's user, called the user image, and a reference picture of a human model from a clothing catalog, calledthe catalog image. Our system produces a real-time photo album depicting how users might look if they wore theclothes and posed for a camera. One of our goals was to design a system that unskilled users could operate, in whichpreprocessing of the user image and system training require only quick, simple interaction.

Title :Moving Object Detection with Background Model based on Spatio-Temporal TextureLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/moving-object-detection-background-model-based-spatio-temporal-texture

Abstract : Background subtraction is a common method for detecting moving objects, but it is yet a difficult problemto distinguish moving objects from backgrounds when these backgrounds change significantly. Hence, we propose amethod for detecting moving objects with a background model that covers dynamic changes in backgrounds utilizinga spatio-temporal texture named "Space-Time Patch", which describes motion and appearance, whereasconventional textures describe appearance only. Our experimental results show the proposed method outperformsone conventional method in three scenes: in an outdoor scene where leaves and branches of a tree are waving inintermittent wind, in an indoor scene where ceiling lights are turned on and off frequently, and in an escalator scenebeside a window facing outdoors where some passengers are leaning over the hand-rail.

Title :Improvements of Driver Fatigue Detection System Based on Eye Tracking and Dynamic Template MatchingLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/driver-fatigue-detection-system-based-eye-tracking-dynamic-template-matching

Abstract : Driver fatigue detection plays an important role in intelligent transportation systems for driving safety.Therefore, it becomes an essential research issue these years. Recently, Horng and Chen proposed a real-time driverfatigue detection system based on eye tracking and dynamic template matching. In their work, the driver fatiguedetection system consists of four parts: face detection, eye detection, eye tracking, and fatigue detection. However,their work suffers from an exhaustive search in eye tracking with the conventional mean absolute difference (MAD)matching function. To remedy the low accuracy in matching and inefficiency in search, in this paper, we first proposetwo new matching functions, the edge map overlapping (EMO) and the edge pixel count (EPC), to enhance matchingaccuracy. In addition, we utilize fast search algorithms, such as the 2D-log search and the three-step searchalgorithms, to expedite search. The experimental results show that the 2D-log search with the EPC matching functionhas the best performance on eye tracking; it only requires 22.29 search points on average to achieve 99.92% correct

rate of eye tracking, as comparing to the original work which requires 441 search points with only 96.01% correct rate.By theoretical analysis, the total amount of computations for eye tracking in the 2D-log search with EPC only takes upto about 10% of the original work. These improvements make the driver fatigue detection system more suitable forimplementations in embedded systems.

Title :Identification of Fault Types for Underground Cable using Discrete Wavelet TransformLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/identification-fault-types-underground-cable-discrete-wavelet-transform

Abstract : In this paper, a technique for identifying the phase with fault appearance in underground cable ispresented. The Wavelet transform has been employed to extract high frequency components superimposed on faultsignals simulated using ATP/EMTP. The coefficients obtained from the Wavelet transform are used in constructing adecision algorithm. Various cases have been investigated so that the algorithm can be implemented. It is found thatthe proposed method can indicate the fault types with satisfactory accuracy.

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews

Title :Brain Tumor Detection using Neural NetworkLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/brain-tumor-detection-neural-network

Abstract : Medical image segmentation plays an important role in treatment planning, identifying tumors, tumorvolume, patient follow up and computer guided surgery. There are various techniques for medical imagesegmentation. This paper presents a image segmentation technique for locating brain tumor(Astrocytoma- A type ofbrain tumor).Proposed work has been divided in two phases-In the first phase MRI image database(Astrocytomagrade I to IV) is collected and then preprocessing is done to improve quality of image. Second-phase includes threesteps-Feature extraction, Feature selection and Image segmentation. For feature extraction proposed work usesGLCM (Grey Level co-occurrence matrix).To improve accuracy only a subset of feature is selected using hybridGenetic algorithm(Genetic Algorithm+fuzzy rough set) and based on these features fuzzy rules and membershipfunctions are defined for segmenting brain tumor from MRI images of .ANFIS is a adaptive network which combinesbenefits of both fuzzy and neural network .Finally, a comparative analysis is performed between ANFIS, neuralnetwork, Fuzzy ,FCM,K-NN, DWT+SOM,DWT+PCA+KN, Texture combined +ANN, Texture Combined+ SVM interms of sensitivity ,specificity ,accuracy.

M.E Computer Science Image Processing Projects

Title :Adaptive Noise Reduction and Image Enhancment using MORPHOLOGICAL TRANSFORMATIONLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/noise-reduction-image-enhancment-morphological-transformation

Abstract :

Title :Traffic Sign Recognition in Disturbing EnvironmentsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/traffic-sign-recognition-disturbing-environments

Abstract : Traffic sign recognition is a difficult task if we aim at detecting and recognizing signs in images capturedfrom unfavorable environments. Complex background, weather, shadow, and other lighting-related problems maymake it difficult to detect and recognize signs in the rural as well as the urban areas. We employ discrete cosinetransform and singular value decomposition for ex-tracting features that defy external disturbances, and comparedifferent designs of detection and classification systems for the task. Experimental results show that our pilot systemsoffer satisfactory performance when tested with very challenging data.

Title :Cartoon Plus Texture Image Inpainting using Coupled Variational Image DecompositionLanguage : Java

Project Link : http://kasanpro.com/p/java/cartoon-plus-texture-image-inpainting-coupled-variational-image-decomposition

Abstract : In this paper, we develop a decomposition model to inpainting problems. Our assumption is that theunderlying image is the superposition of cartoon and texture components. We use the total variation norm and its dualnorm to regularize the cartoon and texture, respectively. We recommend an efficient numerical algorithm based onthe splitting versions of augmented Lagrangian method to solve the problem. The proposed algorithm gives adecomposition of cartoon and texture parts. These two parts can be further used in inpainting problems. Using thedecomposition, segemenation patches(High Resolution Patches) are defined. Filling order of the HR picture fillingorder is computed on the HR picture with the sparsity-based method. The HR patch is then pasted into the missingareas. However, as an overlap with the already synthesized areas is possible. Thus our work focus on implementationof decomposition model and make inpainting at the missing pixels.

Title :Fingerprint Gender Classification using Wavelet Transform and Singular Value DecompositionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/fingerprint-gender-classification-wavelet-transform-singular-value-decomposition

Abstract : A novel method of gender Classification from fingerprint is proposed based on discrete wavelet transform(DWT) and singular value decomposition (SVD). The classification is achieved by extracting the energy computedfrom all the sub-bands of DWT combined with the spatial features of non-zero singular values obtained from the SVDof fingerprint images. K nearest neighbor (KNN) used as a classifier. This method is experimented with the internaldatabase of 3570 fingerprints finger prints in which 1980 were male fingerprints and 1590 were female fingerprints.Fingerwise gender classification is achieved which is 94.32% for the left hand little fingers of female persons and95.46% for the left hand index finger of male persons. Gender classification for any finger of male persons tested isattained as 91.67% and 84.69% for female persons respectively. Overall classification rate is 88.28% has beenachieved.

Title :Brain Tumor Detection using Region based Iterative Reconstruction and SegmentationLanguage : Java

Project Link : http://kasanpro.com/p/java/brain-tumor-detection-region-based-iterative-reconstruction-segmentation

Abstract : X-ray computed tomography (CT) is a powerful tool for noninvasive imaging of time-varying objects.Identifiying tumors from the CT image is a chalanging one. In this paper we proposed a reconstruct method for CTimage and tumors are detected then using edge based segmentation algorithm.. In the past, methods have beenproposed to reconstruct images from continuously changing objects. For discretely or structurally changing objects,however, such methods fail to reconstruct high quality images, mainly because assumptions about continuity are nolonger valid. In this paper, we propose a method to reconstruct structurally changing objects. Starting from theobservation that there exist regions within the scanned object that remain unchanged over time, we introduce aniterative optimization routine that can automatically determine these regions and incorporate this knowledge in analgebraic reconstruction method. And tumor detection was made from the reconstructed image.

M.E Computer Science Image Processing Projects

Title :Automatic graph based approach for prior detection of diabetes and hypertension in retinal imagesLanguage : Java

Project Link : http://kasanpro.com/p/java/automatic-graph-based-prior-detection-diabetes-hypertension-retinal-images

Abstract : Retinal vessels are affected by several systemic diseases, namely diabetes, hypertension, and vasculardisorders. In diabetic retinopathy, the blood vessels often show abnormalities at early stages, as well as vesseldiameter alterations . Changes in retinal blood vessels, such as significant dilatation and elongation of main arteries,veins, and their branches are also frequently associated with hypertension and other cardiovascular pathologies. Theclassification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascularchanges, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes,hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classificationbased on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entirevascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to eachvessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination ofthe graph-based labeling results with a set of intensity features. The features were extracted, including exudates,

bifurcation angle, artery-to-veins diameter ratio, mean artery and veins diameters, form and size of optic disc, andvessel tortuosity. And the identification of diabetes are made by the rule based conditions.

Title :Tumor Tissue Classification using Bayes and SVM ClassifierLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/tumor-tissue-classification-bayes-svm-classifier

Abstract :

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews

Title :A Compressive Sensing based Secure Watermark Detection and Privacy Preserving Storage FrameworkLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/secure-watermark-detection-privacy-preserving-storage-framework

Abstract : Privacy is a critical issue when the data owners outsource data storage or processing to a third partycomputing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requiressimultaneously performing secure watermark detection and privacy preserving multimedia data storage. We thenpropose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols toaddress such a requirement. In our framework, the multimedia data and secret watermark pattern are presented tothe cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacyof the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model.We derive the expected watermark detection performance in the CS domain, given the target image, watermarkpattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performancehas been validated by our experiments. Our theoretical analysis and experimental results show that secure watermarkdetection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signalprocessing and data-mining applications in the cloud.

Title :A New Iterative Triclass Thresholding Technique in Image SegmentationLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/new-iterative-triclass-thresholding-technique-image-segmentation

Abstract : We present a new method in image segmentation that is based on Otsu's method but iteratively searchesfor subregions of the image for segmentation, instead of treating the full image as a whole region for processing. Theiterative method starts with Otsu's threshold and computes the mean values of the two classes as separated by thethreshold. Based on the Otsu's threshold and the two mean values, the method separates the image into threeclasses instead of two as the standard Otsu's method does. The first two classes are determined as the foregroundand background and they will not be processed further. The third class is denoted as a to-be-determined (TBD) regionthat is processed at next iteration. At the succeeding iteration, Otsu's method is applied on the TBD region tocalculate a new threshold and two class means and the TBD region is again separated into three classes, namely,foreground, background, and a new TBD region, which by definition is smaller than the previous TBD regions. Then,the new TBD region is processed in the similar manner. The process stops when the Otsu's thresholds calculatedbetween two iterations is less than a preset threshold. Then, all the intermediate foreground and background regionsare, respectively, combined to create the final segmentation result. Tests on synthetic and real images showed thatthe new iterative method can achieve better performance than the standard Otsu's method in many challengingcases, such as identifying weak objects and revealing fine structures of complex objects while the addedcomputational cost is minimal.

Title :As-Projective-As-Possible Image Stitching with Moving DLTLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/as-projective-as-possible-image-stitching-moving-dlt

Abstract : We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptionsof the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarilysolved by estimating a projective warp -- a model that is justified when the scene is planar or when the views differpurely by rotation. Such conditions are easily violated in practice, and this yields stitching results with ghostingartefacts that necessitate the usage of deghosting algorithms. To this end we propose as-projective-as-possible

warps, i.e., warps that aim to be globally projective, yet allow local non-projective deviations to account for violationsto the assumed imaging conditions. Based on a novel estimation technique called Moving Direct LinearTransformation (Moving DLT), our method seamlessly bridges image regions that are inconsistent with the projectivemodel. The result is highly accurate image stitching, with significantly reduced ghosting effects, thus lowering thedependency on post hoc deghosting.

M.E Computer Science Image Processing Projects

Title :Captcha as Graphical Passwords--A New Security Primitive Based on Hard AI ProblemsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/captcha-graphical-password

Abstract : Many security primitives are based on hard mathematical problems. Using hard AI problems for security isemerging as an exciting new paradigm, but has been underexplored. In this paper, we present a new securityprimitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captchatechnology, which we call Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphicalpassword scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relayattacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP password can befound only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP alsooffers a novel approach to address the well-known image hotspot problem in popular graphical password systems,such as PassPoints, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonablesecurity and usability and appears to fit well with some practical applications for improving online security.

Title :Corruptive Artifacts Suppression for Example-Based Color TransferLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/corruptive-artifacts-suppression-example-based-color-transfer

Abstract : Example-based color transfer is a critical operation in image editing but easily suffers from some corruptiveartifacts in themapping process. In this paper,we propose a novel unified color transfer framework with corruptiveartifacts suppression, which performs iterative probabilistic color mapping with self-learning filtering scheme andmultiscale detail manipulation scheme inminimizing the normalized Kullback-Leibler distance. First, an iterativeprobabilistic color mapping is applied to construct the mapping relationship between the reference and target images.Then, a self-learning filtering scheme is applied into the transfer process to prevent from artifacts and extract details.The transferred output and the extracted multi-levels details are integrated by the measurement minimization to yieldthe final result. Our framework achieves a sound grain suppression, color fidelity and detail appearance seamlessly.For demonstration, a series of objective and subjective measurements are used to evaluate the quality in colortransfer. Finally, a few extended applications are implemented to show the applicability of this framework.

Title :Fingerprint Compression Based on Sparse RepresentationLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/fingerprint-compression-based-sparse-representation

Abstract : A new fingerprint compression algorithm based on sparse representation is introduced. Obtaining anovercomplete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combinationof dictionary atoms. In the algorithm, we first construct a dictionary for predefined fingerprint image patches. For anew given fingerprint images, represent its patches according to the dictionary by computing l0-minimization and thenquantize and encode the representation. In this paper, we consider the effect of various factors on compressionresults. Three groups of fingerprint images are tested. The experiments demonstrate that our algorithm is efficientcompared with several competing compression techniques (JPEG, JPEG 2000, andWSQ), especially at highcompression ratios. The experiments also illustrate that the proposed algorithm is robust to extract minutiae.

Title :How to Estimate the Regularization Parameter for Spectral Regression Discriminant Analysis and its KernelVersion?Language : Matlab

Project Link : http://kasanpro.com/p/matlab/regularization-parameter-spectral-regression-discriminant-analysis

Abstract : Spectral regression discriminant analysis (SRDA) has recently been proposed as an efficient solution tolarge-scale subspace learning problems. There is a tunable regularization parameter in SRDA, which is critical to

algorithm performance. However, how to automatically set this parameter has not been well solved until now. So thisregularization parameter was only set to be a constant in SRDA, which is obviously suboptimal. This paper proposesto automatically estimate the optimal regularization parameter of SRDA based on the perturbation linear discriminantanalysis (PLDA). In addition, two parameter estimation methods for the kernel version of SRDA are also developed.One is derived from the method of optimal regularization parameter estimation for SRDA. The other is to utilize thekernel version of PLDA. Experiments on a number of publicly available databases demonstrate the effectiveness ofthe proposed methods for face recognition, spoken letter recognition, handwritten digit recognition, and textcategorization.

Title :Image Classification Using Multiscale Information Fusion Based on Saliency Driven Nonlinear Diffusion FilteringLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/image-classification-based-fusion-diffusion-filtering

Abstract : In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scalespace in general preserves or even enhances semantically important structures such as edges, lines, or flow-likestructures in the foreground, and inhibits and smoothes clutter in the background. The image is classified usingmultiscale information fusion based on the original image, the image at the final scale at which the diffusion processconverges, and the image at a midscale. Our algorithm emphasizes the foreground features, which are important forimage classification. The background image regions, whether considered as contexts of the foreground or noise to theforeground, can be globally handled by fusing information from different scales. Experimental tests of theeffectiveness of the multiscale space for the image classification are conducted on the following publicly availabledatasets: 1) the PASCAL 2005 dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17 flowers dataset, withhigh classification rates.

M.E Computer Science Image Processing Projects

Title :LBP-Based Edge-Texture Features for Object RecognitionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/lbp-based-edge-texture-features-object-recognition

Abstract : This paper proposes two sets of novel edge-texture features, Discriminative Robust Local Binary Pattern(DRLBP) and Ternary Pattern (DRLTP), for object recognition. By investigating the limitations of Local Binary Pattern(LBP), Local Ternary Pattern (LTP) and Robust LBP (RLBP), DRLBP and DRLTP are proposed as new features.They solve the problem of discrimination between a bright object against a dark background and vice-versa inherentin LBP and LTP. DRLBP also resolves the problem of RLBP whereby LBP codes and their complements in the sameblock are mapped to the same code. Furthermore, the proposed features retain contrast information necessary forproper representation of object contours that LBP, LTP, and RLBP discard. Our proposed features are tested onseven challenging data sets: INRIA Human, Caltech Pedestrian, UIUC Car, Caltech 101, Caltech 256, Brodatz, andKTH-TIPS2- a. Results demonstrate that the proposed features outperform the compared approaches on most datasets.

Title :Learning Layouts for Single-Page Graphic DesignsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/learning-layouts-single-page-graphic-designs

Abstract : This paper presents an approach for automatically creating graphic design layouts using a newenergy-based model derived from design principles. The model includes several new algorithm for analyzing graphicdesigns, including the prediction of perceived importance, alignment detection, and hierarchical segmentation. Giventhe model, we use optimization to synthesize new layouts for a variety of single-page graphic designs. Modelparameters are learned with Nonlinear Inverse Optimization (NIO) from a small number of example layouts. Todemonstrate our approach, we show result for application including generation design layouts in various styles,retargeting designs to new sizes, and improving existing designs. We also compare our automatic results withdesigns created using crowdsourcing and show that our approach performs slightly better than novice designers.

Title :Localization of License Plate Number Using Dynamic Image Processing Techniques And Genetic AlgorithmsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/license-plate-number-localization-using-image-processing-and-genetic-algorithms

Abstract : In this research, a design of a new genetic algorithm (GA) is introduced to detect the locations of theLicense Plate (LP) symbols. An adaptive threshold method has been applied to overcome the dynamic changes ofillumination conditions when converting the image into binary. Connected component analysis technique (CCAT) isused to detect candidate objects inside the unknown image. A scale-invariant Geometric Relationship Matrix (GRM)has been introduced to model the symbols layout in any LP which simplifies system adaptability when applied indifferent countries. Moreover, two new crossover operators, based on sorting, have been introduced which greatlyimproved the convergence speed of the system. Most of CCAT problems such as touching or broken bodies havebeen minimized by modifying the GA to perform partial match until reaching to an acceptable fitness value. Thesystem has been implemented using MATLAB and various image samples have been experimented to verify thedistinction of the proposed system. Encouraging results with 98.4% overall accuracy have been reported for twodifferent datasets having variability in orientation, scaling, plate location, illumination and complex background.Examples of distorted plate images were successfully detected due to the independency on the shape, color, orlocation of the plate.

Title :Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral MasksLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/model-based-edge-detector-spectral-imagery-using-sparse-spatiospectral-masks

Abstract : Two model-based algorithms for edge detection in spectral imagery are developed that specifically targetcapturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity.Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in ascene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials,are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask,producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for everymaterial pair by matching the response of the operator at every pixel with the edge signature for the pair of materials.The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctivefeatures before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imageryfrom the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolorgradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as abenchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG andHySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bandsas input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection.In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to thoserequired by the MCG edge detector.

Title :Modeling of Speaking Rate Influences on Mandarin Speech Prosody and Its Application to SpeakingRate-controlled TTSLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/application-speaking-rate-controlled-tts

Abstract : A new data-driven approach to building a speaking rate-dependent hierarchical prosodic model (SR-HPM),directly from a large prosody-unlabeled speech database containing utterances of various speaking rates, to describethe influences of speaking rate on Mandarin speech prosody is proposed. It is an extended version of the existingHPM model which contains 12 sub-models to describe various relationships of prosodic-acoustic features of speechsignal, linguistic features of the associated text, and prosodic tags representing the prosodic structure of speech. Twomain modifications are suggested. One is designing proper normalization functions from the statistics of the wholedatabase to compensate the influences of speaking rate on all prosodic-acoustic features. Another is modifying theHPM training to let its parameters be speaking-rate dependent. Experimental results on a large Mandarin read speechcorpus showed that the parameters of the SR-HPM together with these feature normalization functions interpreted theeffects of speaking rate onMandarin speech prosody very well. An application of the SR-HPM to design andimplement a speaking rate-controlledMandarin TTS system is demonstrated. The system can generate naturalsynthetic speech for any given speaking rate in awide range of 3.4-6.8 syllables/sec.Two subjective tests,MOSandpreference test,were conducted to compare the proposed system with the popular HTS system. TheMOS scores ofthe proposed system were in the range of 3.58-3.83 for eight different speaking rates, while they were in 3.09-3.43 forHTS. Besides, the proposed system had higher preference scores (49.8%-79.6%) than those (9.8%-30.7%) ofHTS.This confirmed the effectiveness of the speaking rate control method of the proposed TTS system.

M.E Computer Science Image Processing Projects

Title :Multisensor Fusion-Based Concurrent Environment Mapping and Moving Object Detection for IntelligentService RoboticsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/moving-object-detection-intelligent-service-robotics

Abstract : Intelligent service robot development is an important and critical issue for human community applications.With the diverse and complex service needs, the perception and navigation are essential subjects. This investigationfocuses on the synergistic fusion of multiple sensors for an intelligent service robot that not only performsself-localization and mapping but also detects moving objects or people in the building it services. First of all, a newaugmented approach of graph-based optimal estimation was derived for concurrent robot postures and moving objecttrajectory estimate. Moreover, all the moving object detection issues of a robot's indoor navigation are divided andconquered via multisensor fusion methodologies. From bottom to up, the estimation fusion methods are tacticallyutilized to get a more precise result than the one from only the laser ranger or stereo vision. Furthermore, for solvingthe consistent association problem of moving objects, a covariance area intersection belief assignment is applied formotion state evaluation and the complementary evidences such as kinematics and vision features are bothsynergized together to enhance the association efficiency with the evidence fusion method. The proof of concept withexperiments has been successfully demonstrated and analyzed.

Title :Personalized Geo-Specific Tag Recommendation for Photos on Social WebsitesLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/personalized-geo-specific-tag-recommendation-photos-social-websites

Abstract : Social tagging becomes increasingly important to organize and search large-scale community-contributedphotos on social websites. To facilitate generating high-quality social tags, tag recommendation by automaticallyassigning relevant tags to photos draws particular research interest. In this paper, we focus on the personalized tagrecommendation task and try to identify user-preferred, geo-location-specific as well as semantically relevant tags fora photo by leveraging rich contexts of the freely available community-contributed photos. For users and geo-locations,we assume they have different preferred tags assigned to a photo, and propose a subspace learning method toindividually uncover the both types of preferences. The goal of our work is to learn a unified subspace shared by thevisual and textual domains to make visual features and textual information of photos comparable. Considering thevisual feature is a lower level representation on semantics than the textual information, we adopt a progressivelearning strategy by additionally introducing an intermediate subspace for the visual domain, and expect it to haveconsistent local structure with the textual space. Accordingly, the unified subspace is mapped from the intermediatesubspace and the textual space respectively. We formulate the above learning problems into a united form, andpresent an iterative optimization with its convergence proof. Given an untagged photo with its geo-location to a user,the user-preferred and the geo-location-specific tags are found by the nearest neighbor search in the correspondingunified spaces. Then we combine the obtained tags and the visual appearance of the photo to discover thesemantically and visually related photos, among which the most frequent tags are used as the recommended tags.Experiments on a large-scale data set collected from Flickr verify the effectivity of the proposed solution.

Title :Photometric Stereo Using Sparse Bayesian Regression for General Diffuse SurfacesLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/photometric-stereo-using-sparse-bayesian-regression-general-diffuse-surfaces

Abstract : Most conventional algorithms for non-Lambertian photometric stereo can be partitioned into twocategories. The first category is built upon stable outlier rejection techniques while assuming a dense Lambertianstructure for the inliers, and thus performance degrades when general diffuse regions are present. The second utilizescomplex reflectance representations and non-linear optimization over pixels to handle non-Lambertian surfaces, butdoes not explicitly account for shadows or other forms of corrupting outliers. In this paper, we present a purelypixel-wise photometric stereo method that stably and efficiently handles various non-Lambertian effects by assumingthat appearances can be decomposed into a sparse, non-diffuse component (e.g., shadows, specularities, etc.) and adiffuse component represented by a monotonic function of the surface normal and lighting dot-product. This functionis constructed using a piecewise linear approximation to the inverse diffuse model, leading to closed-form estimatesof the surface normals and model parameters in the absence of non-diffuse corruptions. The latter are modeled aslatent variables embedded within a hierarchical Bayesian model such that we may accurately compute the unknownsurface normals while simultaneously separating diffuse from non-diffuse components. Extensive evaluations areperformed that show state-of-the-art performance using both synthetic and real-world images.

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews

Title :Quality Assessment of Stereoscopic 3D Image Compression by Binocular Integration BehaviorsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/quality-assessment-stereoscopic-3d-image-compression-binocular-integration-behaviors

Abstract : The objective approaches of 3D image quality assessment play a key role for the development ofcompression standards and various 3D multimedia applications. The quality assessment of 3D images faces morenew challenges, such as asymmetric stereo compression, depth perception, and virtual view synthesis, than its 2Dcounterparts. In addition, the widely used 2D image quality metrics (e.g., PSNR and SSIM) cannot be directly appliedto deal with these newly introduced challenges. This statement can be verified by the low correlation between thecomputed objective measures and the subjectively measured mean opinion scores (MOSs), when 3D images are thetested targets. In order to meet these newly introduced challenges, in this paper, besides traditional 2D imagemetrics, the binocular integration behaviors--the binocular combination and the binocular frequency integration, areutilized as the bases for measuring the quality of stereoscopic 3D images. The effectiveness of the proposed metricsis verified by conducting subjective evaluations on publicly available stereoscopic image databases. Experimentalresults show that significant consistency could be reached between the measured MOS and the proposed metrics, inwhich the correlation coefficient between them can go up to 0.88. Furthermore, we found that the proposed metricscan also address the quality assessment of the synthesized color-plusdepth 3D images well. Therefore, it is our beliefthat the binocular integration behaviors are important factors in the development of objective quality assessment for3D images.

Title :Robust Semi-Automatic Depth Map Generation in Unconstrained Images and Video Sequences for 2D toStereoscopic 3D ConversionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/depth-map-generation-unconstrained-images

Abstract : We describe a system for robustly estimating synthetic depth maps in unconstrained images and videos,for semi-automatic conversion into stereoscopic 3D. Currently, this process is automatic or done manually byrotoscopers. Automatic is the least labor intensive, but makes user intervention or error correction difficult. Manual isthe most accurate, but time consuming and costly. Noting the merits of both, a semi-automatic method blends themtogether, allowing for faster and accurate conversion. This requires user-defined strokes on the image, or over severalkeyframes for video, corresponding to a rough estimate of the depths. After, the rest of the depths are determined,creating depth maps to generate stereoscopic 3D content, with Depth Image Based Rendering to generate theartificial views. Depth map estimation can be considered as a multi-label segmentation problem: each class is adepth. For video, we allow the user to label only the first frame, and we propagate the strokes using computer visiontechniques. We combine the merits of two well-respected segmentation algorithms: Graph Cuts and Random Walks.The diffusion from Random Walks, with the edge preserving of Graph Cuts should give good results. We generategood quality content, more suitable for perception, compared to a similar framework.

M.E Computer Science Image Processing Projects

Title :Sharing Visual Secrets in Single Image Random Dot StereogramsLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/sharing-visual-secrets-single-image-random-dot-stereograms

Abstract : Visual cryptography schemes (VCSs) generate random and meaningless shares to share and protectsecret images. Conventional VCSs suffer from a transmission risk problem because the noise-like shares will raisethe suspicion of attackers and the attackers might intercept the transmission. Previous research has involved in hidingshared content in halftone shares to reduce these risks, but this method exacerbates the pixel expansion problem andvisual quality degradation problem for recovered images. In this paper, a binocular VCS (BVCS), called the (2,n)-BVCS, and an encryption algorithm are proposed to hide the shared pixels in the single image random dotstereograms (SIRDSs). Because the SIRDSs have the same 2D appearance as the conventional shares of a VCS,this paper tries to use SIRDSs as cover images of the shares of VCSs to reduce the transmission risk of the shares.The encryption algorithm alters the random dots in the SIRDSs according to the construction rule of the (2, n)-BVCSto produce nonpixelexpansion shares of the BVCS. Altering the dots in a SIRDS will degrade the visual quality of thereconstructed 3D objects. Hence, we propose an optimization model that is based on the visual quality requirement ofSIRDSs to develop construction rules for a (2, n)-BVCS that maximize the contrast of the recovered image in theBVCS.

Title :Single-Image Superresolution of Natural Stochastic Textures Based on Fractional Brownian MotionLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/superresolution-natural-stochastic-textures-based-fractional-brownian-motion

Abstract : Texture enhancement presents an ongoing challenge, in spite of the considerable progress made in recent

years. Whereas most of the effort has been devoted so far to enhancement of regular textures, stochastic texturesthat are encountered in most natural images, still pose an outstanding problem. The purpose of enhancement ofstochastic textures is to recover details, which were lost during the acquisition of the image. In this paper, a texturemodel, based on fractional Brownian motion (fBm), is proposed. The model is global and does not entail using imagepatches. The fBm is a self-similar stochastic process. Self-similarity is known to characterize a large class of naturaltextures. The fBm-based model is evaluated and a single-image regularized superresolution algorithm is derived. Theproposed algorithm is useful for enhancement of a wide range of textures. Its performance is compared withsingle-image superresolution methods and its advantages are highlighted.

Title :Weighted KPCA Degree of Homogeneity Amended Nonclassical Receptive Field Inhibition Model for SalientContour Extraction in Low-Light-Level ImageLanguage : Matlab

Project Link : http://kasanpro.com/p/matlab/nonclassical-receptive-field-inhibition-model-salient-contour-extraction

Abstract : The stimulus response of the classical receptive field (CRF) of neuron in primary visual cortex is affectedby its periphery [i.e., non-CRF (nCRF)]. This modulation exerts inhibition, which depends primarily on the correlationof both visual stimulations. The theory of periphery and center interaction with visual characteristics can be applied innight vision information processing. In this paper, a weighted kernel principal component analysis (WKPCA) degree ofhomogeneity (DH) amended inhibition model inspired by visual perceptual mechanisms is proposed to extract salientcontour from complex natural scene in low-light-level image. The core idea is that multifeature analysis can recognizethe homogeneity in modulation coverage effectively. Computationally, a novel WKPCA algorithm is presented toeliminate outliers and anomalous distribution in CRF and accomplish principal component analysis precisely. On thisbasis, a new concept and computational procedure for DH is defined to evaluate the dissimilarity between peripheryand center comprehensively. Through amending the inhibition from nCRF to CRF by DH, our model can reduce theinterference of noises, suppress details, and textures in homogeneous regions accurately. It helps to further avoidmutual suppression among inhomogeneous regions and contour elements. This paper provides an improvedcomputational visual model with high-performance for contour detection from cluttered natural scene in night visionimage.

Title :Coupled Variational Image Decomposition and Restoration Model for Blurred Cartoon-Plus-Texture ImagesWith Missing PixelsLanguage : Java

Project Link : http://kasanpro.com/p/java/cartoon-plus-texture-image-decomposition-restoration

Abstract : In this paper, we develop a decomposition model to restore blurred images with missing pixels. Ourassumption is that the underlying image is the superposition of cartoon and texture components. We use the totalvariation norm and its dual norm to regularize the cartoon and texture, respectively. We recommend an efficientnumerical algorithm based on the splitting versions of augmented Lagrangian method to solve the problem.Theoretically, the existence of a minimizer to the energy function and the convergence of the algorithm areguaranteed. In contrast to recently developed methods for deblurring images, the proposed algorithm not only givesthe restored image, but also gives a decomposition of cartoon and texture parts. These two parts can be further usedin segmentation and inpainting problems. Numerical comparisons between this algorithm and some state-of-the-artmethods are also reported.

Title :Region-Based Iterative Reconstruction of Structurally Changing Objects in CTLanguage : Java

Project Link : http://kasanpro.com/p/java/region-based-iterative-reconstruction-structurally-changing-objects-ct

Abstract : X-ray computed tomography (CT) is a powerful tool for noninvasive imaging of time-varying objects. In thepast, methods have been proposed to reconstruct images from continuously changing objects. For discretely orstructurally changing objects, however, such methods fail to reconstruct high quality images, mainly becauseassumptions about continuity are no longer valid. In this paper, we propose a method to reconstruct structurallychanging objects. Starting from the observation that there exist regions within the scanned object that remainunchanged over time, we introduce an iterative optimization routine that can automatically determine these regionsand incorporate this knowledge in an algebraic reconstruction method. The proposed algorithm was validated onsimulation data and experimental ?CT data, illustrating its capability to reconstruct structurally changing objects moreaccurately in comparison to current techniques.

M.E Computer Science Image Processing Projects

Title :An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images

Language : Java

Project Link : http://kasanpro.com/p/java/an-automatic-graph-based-artery-vein-classification-retinal-images

Abstract : The classification of retinal vessels into artery/vein (A/V) is an important phase for automating thedetection of vascular changes, and for the calculation of characteristic signs associated with several systemicdiseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automaticapproach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposedmethod classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigningone of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performedthrough the combination of the graph-based labeling results with a set of intensity features. The results of thisproposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%,and 89.8% are obtained for the images of the INSPIREAVR, DRIVE, and VICAVR databases, respectively. Theseresults demonstrate that our method outperforms recent approaches for A/V classification.

http://kasanpro.com/ieee/final-year-project-center-kanchipuram-reviews