seminar report_image processing
TRANSCRIPT
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SEMINAR REPORT
ImageProcessing
IntroductionandApplication
Guided By By
Mayank Srivastava Ravi Kumar Verma
Asst. Professor ECE 3rd
Year
ECE Dept. RKGIT 0803331092
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CERTIFICATE
This is to certify that Ravi Kumar Verma of ECE 6 th semester has worked hard
under my guidance on the seminar topic assigned to him.
He has been honest and determined throughout the seminar conducted.
GUIDE FACULTY
MR. MAYANK SRIVASTAVA
ASST. PROFESSOR
DEPT. OF ECE, RKGIT
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ACKNOWLEDGMENT
I extend my sincere gratitude towards Prof. K.K. Tripathi Head ofDepartment for giving us his invaluable knowledge and wonderful technical
guidance.
I express my thanks to Mayank Srivastava Sir, who guided me and
provided with all the usefull information for presenting this seminar.
I also thank all the other faculty members of ECE department and my
friends for their help and support.
Ravi Kumar Verma
ECE 3rd
year
0803331092
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ABSTRACT
Image Processing, in its broadest and most literal sense, aims to address the goal of providing
practical, reliable and affordable means to allow machines to cope with images while assisting man
in his general endeavors.
The term image processing itself has become firmly associated with the much more objective of
modifying images such that they are either:
a. Corrected for errors introduced during acquisition or transmission (restoration); or
b. Enhanced to overcome the weakness of human visual system (enhancement)
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TABLE OF CONTENTS
(a)Acknowledgment
(b)Abstract
1. Introduction
2. Image and Image Processing
3. Vision and Computer vision
4. Types of image processing5. Steps involved in image processing
6. Components of image processing
7. Image sensors(CCD and CMOS)
8. Applications
9. Conclusion
10. References
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Introduction
Images are a vital and integral part of everyday life. On an individual or person-to-person basis,images are used to reason, interpret, illustrate, represent, memorize, educate, communicate,
evaluate, navigate, survey, entertain, etc. We do this continuously and almost entirely without
conscious effort. As man builds machines to facilitate his ever more complex lifestyle, the onlyreason for NOT providing them with the ability to exploit or transparently convey such images isa weakness of available technology.
Interests in image processing processing stems from two principal application areas:
a) Improvement of pictorial information for better human interpretationb) Processing of scene data for autonomous machine perception
One of the first applications of image processing techniques in the first category was inimproving digitized newspaper sent by submarine cable between London and Newyork. From
then till these days, image processing is continuously improving human vision. The field has
grown so vigorously that it is now used to solve variety of problems ranging from improvingvision to space program, in geographical information systems, in medicines, in surveillance etc.Geographers use the same technique to study pollution patterns from aerial and satellite imagery.
Image enhancement and restoration techniques are used to process degraded images ofunrecoverable objects or experimental results too expensive to duplicate. In archaeology, image
processing methods have successfully restored blurred pictures that were the only availablerecords of rare artifacts lost or damaged after photographed. In physics and related fields,
computer techniques routinely enhance images of experiments in areas such as high energyplasma and electron microscopy. Similarly successful applications of image processing can be
found in astronomy, biology, nuclear medicine, law enforcement, defense, and industrialapplications.
Typical problems in machine perception that routinely utilize image processing techniques areautomatic character recognition, industrial machine vision for product assembly and inspection,
military recognizance, automatic processing of fingerprints, screening of x-rays and bloodsamples, and machine processing of aerial and satellite imagery for weather prediction and crop
assessment.
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IMAGEAn image (Latin: imago) is an artifact, for example a two-dimensional picture, that has a similarappearance to some subjectusually a physical object or a person.
Mathematically image can be defined as,
Image is a two dimensional light intensity function, f(x, y), where the value of f at a spatiallocation (x, y) is the intensity of the image at that point.
Digital image is obtained by sampling and quantizing the function f(x, y).
The function f(x, y) can be a measure of the reflected light (photography), X-ray attenuation (X-Rays) or any other physical parameter.
Digital Image is actually an image discretized both in spatial coordinates and brightness.A digital image can be considered a matrix whose row and column indices identify a point in the
image and the corresponding matrix element value identifies the gray level at that point. The
elements of such digital array are called image elements, picture elements, pixels, or pels.
IMAGE PROCESSING
In electrical engineering and computer science, image processing is any form of signal forwhich the input is an image, such as a photograph or video frame; the output of image processing
may be either an image or, a set of characteristics or parameters related to the image. Mostimage-processing techniques involve treating the image as a two-dimensional signal and
applying standard signal-processing techniques to it.
In short,Act of examining images for the purpose of identifying objects and judging their significance.
An image may be considered to contain sub-images sometimes referred to as regions-of-interest,ROIs, or simply regions. This concept reflects the fact that images frequently contain collections
of objects each of which can be the basis for a region. In a sophisticated image processing systemit should be possible to apply specific image processing operations to selected regions. Thus one
part of an image (region) might be processed to suppress motion blur while another part might beprocessed to improve color rendition.
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WHY DO WE NEED IMAGE PROCESSING?
A) Improvement of pictorial information for human interpretation
B) Processing of scene data for autonomous machine perception
Improvementofpictorialinformationforhumaninterpretation
A)Involved selection of printing procedures and distribution of brightnesslevels
B)Improvements on processing methods for transmitted digital pictures
Applicationareasincludea) Archeology
b) Astronomy
c) Biology
d) Industrial Applications
e) Law enforcements
f) Medical Imaging
g) Space program etc.
Processingofscenedataforautonomousmachineperception
Focuses on procedures for extracting from image information in a form suitable for
computer processing.
NOTE: Often this information bears little resemblance to visual features that
human beings use in interpreting the content of an image.
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Applicationareasinclude:
a) Automatic Optical Character Recognition
b)Machine vision for product assembly and inspection
c) Military recognizance
d)Automatic fingerprint matching etc.
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Visionand ComputerVision
Whatever human eyes see and then perceive the world around
- VISION
To duplicate human eye by electronically perceiving and understanding the imageby any means
COMPUTER VISION
Computer Vision
Vision
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TYPES OF IMAGE PROCESSING
Based on the mode of techniques used image processing can be broadly categorized into
following three types:
A)Analog Image ProcessingB)Digital Image ProcessingC)Optical Image Processing
ANALOG IMAGE PROCESSINGIs any image processing task conducted on two-dimensional analog signals by analog
means.
DIGITAL IMAGE PROCESSINGIs the use of computer algorithms to perform image processing on digital images.
OPTICALIMAGEPROCESSINGIs the use of optical techniques to process image for increasing clarity and extracting
information from the image.
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BASED ON THE TRANSFORMATIONS IMAGE PROCESSING
IS CLASSIFIED INTO FOLLOWIN TYPES
a) Image-to-image transformation
b) Image-to-information transformationc) Information-to-image transformation
IMAGE TO IMAGE TRANSFORMATIONEnhancement (make image more useful, pleasing)Restoration (DE blurring, grid, line removal)Geometry (scaling, sizing, zooming, morphing etc.)
IMAGE TO INFORMATION TRANSFORMATION Image statistics (histograms) Image compression Image Analysis (segmentation, feature extraction)Computer aided detection and diagnosis (CAD)
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INFORMATION TO IMAGE TRANSFORMATION Depression of compressed image data
Reconstruction of image Computer graphics, animation and virtual reality
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STEPS INVOVED IN IMAGE PROCESSING
Image processing encompasses a broad range of hardware, software, and the theoretical
underpinnings.
Following flow diagram clearly depicts the important steps involved in image processing:
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IMAGE ACQUISTIONThe first step in the process is image acquisition-that is, to acquire a digital image. To do so
requires following elements:
a) Imaging Sensors
b) Digitizer
Imaging sensors acquires image and the digitizer converts that image into computer
understandable language of digital form. The imaging sensor could be a monochrome or color
TV camera that produces an entire image of the problem domain every 1/30 sec. The imaging
sensor could also be a line camera that produces a single image line at a time. In this case the
objects motion past the line scanner produces a two-dimensional image. If the output of camera
or the other imaging sensor is not in digital form, that is achieved by an ADC (analog to digital
converter). The nature if the sensor and the image it produces are determined by the application.
For ex/-mail reading applications greatly rely on line-scan cameras.
PREPROCESSING
After a digital image has been acquired, the next step deals with the preprocessing of the image.
The key function of preprocessing is to improve the image in ways that increase the chances for
success of other processes. Mainly, preprocessing deals with the techniques for enhancing
contrast, removing noise, and isolating regions whose textures indicate a likelihood of
alphanumeric information.
SEGMENTATION
The next stage deals with segmentation. Broadly defined, segmentation partitions an input image
into its constituent parts or objects. In general, autonomous segmentation is one of the most
difficult tasks in digital image processing. On the one hand a rugged segmentation brings the
process a long way towards successful solution of an imaging problem.On the other hand erratic
segmentation results always into eventual failure.
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The output of the segmentation is raw pixel data, constituting either the boundary of a region or
all the points in the region itself. In either case, converting data to a form suitable to computer
processing is necessary.
Boundary representation is appropriate when the focus is on external shape characteristics, such
as corners or inflections. Regional representations are appropriate when the focus is on internal
properties, such as textures or skeletal shape. In some situations both representations may co-
exist.
REPRESENTATION AND DESCRIPTION
Choosing a representation is only a part of the solution for transforming raw data into a form
suitable for subsequent computer processing. A method must also be specified for describing the
data so that features of interest are highlighted.
Description also called feature selection, deals with extracting features that result in some
quantitative information of interest or features that are basic for differentiating one class of
object from other.
RECOGNITION AND INTERPRETATION
The last stage involves recognition and interpretation. Recognition is the process that assigns a
label to an object based on the information provided by its descriptors. Interpretation involves
assigning meaning to an ensemble of recognized objects. In terms of example, identifying a
character as, say, a c requires associating the descriptors for that character with the label c.
Interpretation attempts to assign meaning to a set of labeled entities.
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KNOWLEDGE BASE
Knowledge about a problem domain is coded into an image processing in the form of knowledge
database. This knowledge base may be as simple as detailing regions of an image where the
information of interest is known to be located. It can be quite complex too, such as aninterrelated list of all major possible defects in a materials inspection problem or an image
database containing high resolution satellite images of a region in connection with change-
detection applications.
In addition to this, knowledge database also controls the interaction between modules.
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COMPONENTS OF IMAGE PROCESSING
Image Sensors
Image Displays
Image Processing Software(OpenCV,Matlab,CIMG)
Image Processing Hardware
Memory
IMAGE SENSORS
Sensors are device which convert illumination energy into digitized form.
An imagesensor is a device that converts an optical image to an electric signal. It is used mostly
in digital cameras and other imaging devices. Early sensors were video camera tubes but a
modern one is typically a charge-coupled device (CCD) or a complementary metaloxide
semiconductor (CMOS) active pixel sensor.
Following are the sensors which are used dominantly in image processing:
Charge Couple Devices (CCD)
Complementary MOSFET (CMOS)
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CHARGE COUPLED DEVICES (CCD)
A charge-coupled device(CCD) isa device for the movement of electrical charge, usually from
within the device to an area where the charge can be manipulated, for example conversion into
a digital value. This isachieved by "shifting" the signalsbetween stages within the device one at
a time. CCDs move charge between capacitive bins in the device, with the shift allowing for the
transfer of charge between bins.
Often the device is integrated with an image sensor, such asa photoelectric device to produce
the charge that is being read, thus making the CCD a major technology for digital image.
Although CCDsare not the only technology to allow for light detection, CCDsare widely used in
professional, medical, and scientific applications where high-quality image data is required.
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COMPLIMENTARY MOSFETs SENSORS (CMOS)
CMOS sensors also known as ACTIVE PIXEL SENSORS(ASP), uses integrated circuits like
transistorsat each pixel that amplify and move the charge using more traditional wires.
The CMOS approach is more flexible as each pixel can be read individually.
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CCD VS CMOS
Most digital still cameras use either a CCD image sensor or a CMOS sensor. Both types of
sensor accomplish the same task of capturing light and converting it into electrical signals.
A CCD is an analog device. When light strikes the chip it is held as a small electrical charge in
each photo sensor. The charges are converted to voltage one pixel at a time as they are read from
the chip. Additional circuitry in the camera converts the voltage into digital information.
A CMOS chip is a type of active sensor pixel (ASP) made using the CMOS semiconductor
process. Extra circuitry next to each photo sensor converts the light energy to a voltage.
Additional circuitry on the chip may be included to convert the voltage to digital data.
Neither technology has a clear advantage in image quality. On one hand, CCD sensors are more
susceptible to vertical smear from bright light sources when the sensor is overloaded; high-
end CCDs in turn do not suffer from this problem.
CMOS can potentially be implemented with fewer components, use less power, and/or provide
faster readout than CCDs. CCD is a more mature technology and is in most respects the equal of
CMOS. CMOS sensors are less expensive to manufacture than CCD sensors.
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APPLICATIONS
Medicine
Defense
Meteorology
Environmental science
Manufacture
Surveillance
Crime investigation
Script Recognition
Optical Character RecognitionHandwritten Signature Verification
One of the first applications of image processing techniques in the first category was in
improving digitized newspaper sent by submarine cable between London and New York. From
then till these days, image processing is continuously improving human vision. The field has
grown so vigorously that it is now used to solve variety of problems ranging from improving
vision to space program, in geographical information systems, in medicines, in surveillance etc.
Geographers use the same technique to study pollution patterns from aerial and satellite imagery.
Image enhancement and restoration techniques are used to process degraded images of
unrecoverable objects or experimental results too expensive to duplicate. In archaeology, image
processing methods have successfully restored blurred pictures that were the only available
records of rare artifacts lost or damaged after photographed. In physics and related fields,
computer techniques routinely enhance images of experiments in areas such as high energy
plasma and electron microscopy.
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CONCLUSION
Using image processing techniques, we can sharpen the images, contrast to make a graphicdisplay more useful for display, reduce amount of memory requirement for storing image
information, etc., due to such techniques, image processing is applied in recognition of images
as in factory floor quality assurance systems; image
e
nhance
me
nt, as in satellitereconnaissance systems; image synthesis as in law enforcement suspect identification systems,and imageconstruction as in plastic surgery design systems.
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REFERENCES
[1] Digital Image Processing, Third Edition, Gonzalez[2] Wikipedia
[3] Awcock G.W. & Thomas R. (1996) Applied Image Processing.[4] Sid Ahmed (1995) Image Processing.
[5] William K. Pratt (1978) Digital Image Processing.
[6] Christopher Watkins, Alberto Sadun, Stephen Marenka Mordern
Image Processing.
[7] Maher A. Sid-Ahmed Image Processing.
[8] G.W.Awcock, R. Thomas Applied Image Processing.
[9] Google