matlab image processing_2013_ieee

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1. Robust Face Recognition for Uncontrolled Pose and Illumination Changes

Abstract — Face recognition has made significant advances in the last decade,

but robust commercial applications are still lacking. Current

authentication/identification applications are limited to controlled settings, e.g.,

limited pose and illumination changes, with the user usually aware of being

screened and collaborating in the process. Among others, pose and illumination

changes are limited. To address challenges from looser restrictions, this paper

proposes a novel framework for real-world face recognition in uncontrolled

settings named Face Analysis for Commercial Entities (FACE). Its robustness

comes from normalization (“correction”) strategies to address pose and

illumination variations. In addition, two separate image quality indices

quantitatively assess pose and illumination changes for each biometric query,

before submitting it to the classifier. Samples with poor quality are possibly

discarded or undergo a manual classification or, when possible, trigger a new

capture. After such filter, template similarity for matching purposes is measured

using a localized version of the image correlation index. Finally, FACE adopts

reliability indices, which estimate the “acceptability” of the final identification

decision made by the classifier.

2. Reversible Watermarking Based on Invariant Image Classification and

Dynamic Histogram Shifting

Abstract — In this paper, we propose a new reversible watermarking scheme.

One first contribution is a histogram shifting modulation which adaptively takes

care of the local specificities of the image content. By applying it to the image

prediction-errors and by considering their immediate neighborhood, the scheme

we propose inserts data in textured areas where other methods fail to do so.

Furthermore, our scheme makes use of a classification process for identifying

parts of the image that can be watermarked with the most suited reversible


modulation. This classification is based on a reference image derived from the

image itself, a prediction of it, which has the property of being invariant to the

watermark insertion. In that way, the watermark embedder and extractor remain

synchronized for message extraction and image reconstruction.

3. Automatic Detection and Reconstruction of Building Radar Footprints

From Single VHR SAR Images

Abstract—The spaceborne synthetic aperture radar (SAR) systems Cosmo-

SkyMed, TerraSAR-X, and TanDEM-X acquire imagery with very high spatial

resolution (VHR), supporting various important application scenarios, such as

damage assessment in urban areas after natural disasters. To ensure a reliable,

consistent, and fast extraction of the information from the complex SAR scenes,

automatic information extraction methods are essential. Focusing on the

analysis of urban areas, which is of prime interest of VHR SAR, in this paper,

we present a novel method for the automatic detection and 2-D reconstruction

of building radar footprints from VHR SAR scenes. Unlike most of the

literature methods, the proposed approach can be applied to single images. The

method is based on the extraction of a set of low-level features from the images

and on their composition to more structured primitives using a production

system. Then, the concept of semantic meaning of the primitives is introduced

and used for both the generation of building candidates and the radar footprint

reconstruction. The semantic meaning represents the probability that a primitive

belongs to a certain scattering class (e.g., double bounce, roof, facade) and has

been defined in order to compensate for the lack of detectable features in single

images. Indeed, it allows the selection of the most reliable primitives and

footprint hypotheses on the basis of fuzzy membership grades.


4. Interactive Segmentation for Change Detection in Multispectral Remote-

Sensing Images

Abstract—In this letter, we propose to solve the change detection (CD) problem

in multitemporal remote-sensing images using interactive segmentation

methods. The user needs to input markers related to change and no-change

classes in the difference image. Then, the pixels under these markers are used

by the support vector machine classifier to generate a spectral-change map. To

enhance further the result, we include the spatial contextual information in the

decision process using two different solutions based on Markov random field

and level-set methods.

5. Estimating Information from Image Colors: An Application to Digital

Cameras and Natural Scenes

Abstract—The colors present in an image of a scene provide information about

its constituent elements. But the amount of information depends on the imaging

conditions and on how information is calculated. This work had two aims. The

first was to derive explicitly estimators of the information available and the

information retrieved from the color values at each point in images of a scene

under different illuminations.

6. Airborne Vehicle Detection in Dense Urban Areas Using HoG Features

Abstract—Vehicle detection has been an important research field for years as

there are a lot of valuable applications, ranging from support of traffic planners

to real-time traffic management. Especially detection of cars in dense urban

areas is of interest due to the high traffic volume and the limited space. In city

areas many car-like objects (e.g., dormers) appear which might lead to

confusion. Additionally, the inaccuracy of road databases supporting the

extraction process has to be handled in a proper way. This paper describes an


integrated real-time processing chainwhich utilizes multiple occurrence of

objects in images.

7. Histology Image Retrieval in Optimized Multifeature Spaces

Abstract—Content-based histology image retrieval systems have shown great

potential in supporting decision making in clinical activities, teaching, and

biological research. In content-based image retrieval, feature combination plays

a key role. It aims at enhancing the descriptive power of visual features

corresponding to semantically meaningful queries. It is particularly valuable in

histology image analysis where intelligent mechanisms are needed for

interpreting varying tissue composition and architecture into histological

concepts. This paper presents an approach to automatically combine

heterogeneous visual features for histology image retrieval. The aim is to obtain

the most representative fusion model for a particular keyword that is associated

with multiple query images. The core of this approach is a multiobjective

learning method, which aims to understand an optimal visual-semantic

matching function by jointly considering the different preferences of the group

of query images. The task is posed as an optimization problem, and a

multiobjective optimization strategy is employed in order to handle potential

contradictions in the query images associated with the same keyword.

8. Automatic License Plate Recognition (ALPR)

Abstract—Automatic license plate recognition (ALPR) is the extraction of

vehicle license plate information from an image or a sequence of images. The

extracted information can be used with or without a database in many

applications, such as electronic payment systems (toll payment, parking fee

payment), and freeway and arterial monitoring systems for traffic surveillance.

The ALPR uses either a color, black and white, or infrared camera to take

images. The quality of the acquired images is a major factor in the success of


the ALPR. ALPR as a reallife application has to quickly and successfully

process license plates under different environmental conditions, such as indoors,

outdoors, day or night time. It should also be generalized to process license

plates from different nations, provinces, or states. These plates usually contain

different colors, are written in different languages, and use different fonts; some

plates may have a single color background and others have background images.

The license plates can be partially occluded by dirt, lighting, and towing

accessories on the car.

9. Context-Based Hierarchical Unequal Merging for SAR Image

Segmentation

Abstract—This paper presents an image segmentation method named Context-

based Hierarchical Unequal Merging for Synthetic aperture radar (SAR) Image

Segmentation (CHUMSIS), which uses superpixels as the operation units

instead of pixels. Based on the Gestalt laws, three rules that realize a new and

natural way to manage different kinds of features extracted from SAR images

are proposed to represent superpixel context. The rules are prior knowledge

from cognitive science and serve as top-down constraints to globally guide the

superpixel merging. The features, including brightness, texture, edges, and

spatial information, locally describe the superpixels of SAR images and are

bottom-up forces. While merging superpixels, a hierarchical unequalmerging

algorithm is designed, which includes two stages: 1) coarse merging stage and

2) fine merging stage. The merging algorithm unequally allocates computation

resources so as to spend less running time in the superpixels without ambiguity

and more running time in the superpixels with ambiguity.


10. Context-Dependent Logo Matching and Recognition

Abstract—We contribute, through this paper, to the design of a novel

variational framework able to match and recognize multiple instances of

multiple reference logos in image archives. Reference logos and test images are

seen as constellations of local features (interest points, regions, etc.) and

matched by minimizing an energy function mixing: 1) a fidelity term that

measures the quality of feature matching, 2) a neighborhood criterion that

captures feature co-occurrence/geometry, and 3) a regularization term that

controls the smoothness of the matching solution.

11. Human Detection in Images via Piecewise Linear Support Vector

Machines

Abstract — Human detection in images is challenged by the view and posture

variation problem. In this paper, we propose a piecewise linear support vector

machine (PL-SVM) method to tackle this problem. The motivation is to exploit

the piecewise discriminative function to construct a nonlinear classification

boundary that can discriminate multiview and multiposture human bodies from

the backgrounds in a high-dimensional feature space. A PL-SVM training is

designed as an iterative procedure of feature space division and linear SVM

training, aiming at the margin maximization of local linear SVMs. Each

piecewise SVM model is responsible for a subspace, corresponding to a human

cluster of a special view or posture. In the PL-SVM, a cascaded detector is

proposed with block orientation features and a histogram of oriented gradient

features. Extensive experiments show that compared with several recent SVM

methods, our method reaches the state of the art in both detection accuracy and

computational efficiency, and it performs best when dealing with low-resolution

human regions in clutter backgrounds.


12. Learning-based, automatic 2D-to-3D image and video conversion

Abstract — Despite a significant growth in the last few years, the availability of

3D content is still dwarfed by that of its 2D counterpart. In order to close this

gap, many 2D-to-3D image and video conversion methods have been proposed.

Methods involving human operators have been most successful but also time-

consuming and costly. Automatic methods, that typically make use of a

deterministic 3D scene model, have not yet achieved the same level of quality

for they rely on assumptions that are often violated in practice. In this paper, we

propose a new class of method that are based on the radically different approach

of learning the 2D-to-3D conversion from examples. We develop a method

based on globally estimating the entire depth map of a query image directly

from a repository of 3D images (image + depth pairs or stereopairs) using a

nearest-neighbor regression type idea. We demonstrate both the efficacy and the

computational efficiency of our methods on numerous 2D images and discuss

their drawbacks and benefits. Although far from perfect, our results demonstrate

that repositories of 3D content can be used for effective 2D-to-3D image

conversion. An extension to video is immediate by enforcing temporal

continuity of computed depth maps.

13. Automated Biometric Voice-Based Access Control in ATM

Abstract — An automatic teller machine requires a user to pass an identity test

before any transaction can be granted. The current method available for access

control in ATM is based on smartcard. Efforts were made to conduct an

interview with structured questions among the ATM users and the result

proofed that a lot of problems was associated with ATM smartcard for access

control. Among the problems are; it is very difficult to prevent another person

from attaining and using a legitimate persons card, also conventional smartcard

can be lost, duplicated, stolen or impersonated with accuracy. To address the


problems, the paper proposed the use of biometric voice-based access control

system in automatic teller machine. In the proposed system, access will be

authorized simply by means of an enroll user speaking into a microphone

attached to the automatic teller machine. There are 2 phases in implementation

of the proposed system: first training phase, second testing or operational phase.

14. Steganography using Genetic Algorithm along with Visual

Cryptography

Abstract— Image steganography is an emerging field of research for secure

data hiding and transmission over networks. The proposed system provides the

best approach for Least Significant Bit (LSB) based steganography using

Genetic Algorithm (GA) along with Visual Cryptography (VC). Original

message is converted into cipher text by using secret key and then hidden into

the LSB of original image. Genetic Algorithm and Visual Cryptography has

been used for enhancing the security. Genetic Algorithm is used to modify the

pixel location of stego image and the detection of this message is complex.

Visual Cryptography is used to encrypt the visual information. It is achieved by

breaking the image into two shares based on a threshold. The performance of

the proposed system is experimented by performing steganalysis and

conducting benchmarking test for analysing the parameters like Mean Squared

Error (MSE) and Peak Signal to Noise Ratio (PSNR). The main aim of this

paper is to design the enhanced secure algorithm which uses both

steganography using Genetic Algorithm and Visual Cryptography to ensure

improved security and reliability.

15. Human Skeleton Identification Methods to Reduce Uncomfortable


Light from a Digital Projector

Abstract-- When a speaker stands in front of a projector screen for a

presentation, the eyes will be hurt by the direct light from the digital projector.

This paper proposes a design to reduce the strong light by projecting a black

round mask on the speaker's head. The black round mask is superimposed to the

slide frame by the software of this design and the mask traces the speaker’s

head. The Webcam captures the images from the speaker with the projector

screen. The location of the speaker’s head is determined. This design efficiently

continues to trace the head location. The computer uses this head location and

superimposes a black round mask to reduce the uncomfortable feeling caused

by the strong light of the projector.

16. IMAGE STITCHING WITH COMBINED MOMENT INVARIANTS

AND SIFT FEATURES

Abstract - Image stitching is used to combine multiple photographic images

from camera network with overlapping field of view to produce panoramic

view. With image stitching, the view is enlarged and the amount of information

increases with the no. of images that are stitched. In the existing methods, the

whole images from the adjacent views are considered thus leads to increase in

both time and computational complexity. In this paper, an approach for image

stitching using invariant moments combined with SIFT features is presented to

reduce the time and computational complexity. It is observed that only a small

portion of the adjacent view images are overlapped. Hence, the proposed

method aims in detecting overlapping portion for extracting matching points.

The overlapping regions are determined using gradient based dominant edge

extraction and invariant moments. In the deduced region, the SIFT (Shift

Invariant Feature Transform) features are extracted to determine the matching

features. The registration is carried on with RANSAC (Random Sample


Consensus) algorithm and final output mosaic is obtained by warping the

images. The proposed approach results in reduced time and computational when

compared to existing methods.

17. Vertical-Edge-Based Car-License-Plate Detection Method

Abstract—This paper proposes a fast method for car-licenseplate detection

(CLPD) and presents three main contributions. The first contribution is that we

propose a fast vertical edge detection algorithm (VEDA) based on the contrast

between the grayscale values, which enhances the speed of the CLPD method.

After binarizing the input image using adaptive thresholding (AT), an

unwanted-line elimination algorithm (ULEA) is proposed to enhance the image,

and then, the VEDA is applied. The second contribution is that our proposed

CLPD method processes very-low-resolution images taken by a web camera.

After the vertical edges have been detected by the VEDA, the desired plate

details based on color information are highlighted. Then, the candidate region

based on statistical and logical operations will be extracted. Finally, an LP is

detected. The third contribution is that we compare the VEDA to the Sobel

operator in terms of accuracy, algorithm complexity, and processing time.


Bio-Medical Based Image Processing

18. Lossless medical image compression by IWT

Abstract - The proposed work is to compress the medical data without any

loss(i.e. lossless). Medical information is either in multidimensional or multi-

resolution form, this creates enormous amount of data. Retrieval, Efficient

storage, management and transmission of this voluminous data are highly

complex. This technique combines integer transforms and JPEGLS Prediction

to enhance the performance of lossless compression.

19. Analyzing Macular Edema In Diabetic Patients

Abstract— Diabetic macular edema (DME) is an advanced symptom of diabetic

retinopathy and can lead to irreversible vision loss. In this paper, a two-stage

methodology for the detection and classification of DME severity from color

fundus images is proposed. DME detection is carried out via a supervised

learning approach using the normal fundus images. A feature extraction

technique is introduced to capture the global characteristics of the fundus

images and discriminate the normal from DME images. Disease severity is

assessed using the neural networks.

20. Wavelet Based Image Fusion for Detection of Brain Tumor

Abstract— Brain tumor, is one of the major causes for the increase in mortality

among children and adults. Detecting the regions of brain is the major challenge

in tumor detection. In the field of medical image processing, multi sensor

images are widely being used as potential sources to detect brain tumor. In this

paper, a wavelet based image fusion algorithm is applied on the Magnetic

Resonance (MR) images and Computed Tomography (CT) images which are

used as primary sources to extract the redundant and complementary

information in order to enhance the tumor detection in the resultant fused


image. The main features taken into account for detection of brain tumor

are location of tumor and size of the tumor, which is further optimized

through fusion of images using various wavelet transforms parameters. We

discuss and enforce the principle of evaluating and comparing the

performance of the algorithm applied to the images with respect to various

wavelets type used for the wavelet analysis. The performance efficiency of the

algorithm is evaluated on the basis of PSNR values. The obtained results are

compared on the basis of PSNR with gradient vector field and big bang

optimization. The algorithms are analyzed in terms of performance with respect

to accuracy in estimation of tumor region and computational efficiency of the

algorithms.

Power Systems

21. Synchronous Detection and Digital control of Shunt Active Power Filter

in Power Quality Improvement

Abstract—Power Quality means to maintain purely sinusoidal current wave

form in phase with a purely sinusoidal voltage wave form. Power quality

improvement using traditional compensation methods include many

disadvantages like electromagnetic interference, possible resonance, fixed

compensation, bulkiness etc. So power system and power electronic engineers

need to develop adjustable and dynamic solutions using custom power devices.

These power conditioning equipments use static power electronic converters to

improve the power quality of distribution system customers. The devices

include Active Power Filter (APF), dynamic voltage restorer (DVR) and

Unified Power Quality Conditioner (UPQC). APF is a compensator used to

eliminate the disturbances in current. There are basically two types of APFs: the

shunt type and the series type. This paper examines the control of Shunt Active


Power Filter (SAPF) from two different aspects: Synchronous Detection

Method (SDM) and digital control based on instantaneous power theory (p-q

theory). Simulation results using MATLAB SIMULINK demonstrates the

application of these methods to the control of APF. Moreover, this work shows

that digital control provides better power quality improvement than SDM.

matlab image processing_2013_ieee

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