Edge Detection Using Vector Operators

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  • Topic: Image processing, Color space, RGB color model
  • Pages : 27 (6742 words )
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  • Published : January 9, 2013
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EDGE DETECTION IN
COLOUR IMAGE

REPORT
MINOR PROJECT- I

DEPARTMENT OF ELECTRONICS AND COMMUNICATION
ENGINEERING, JAYPEE INSTITUTE OF INFORMATION
TECHNOLOGY, NOIDA

SUBMITTED BY:
SHEFALI JAIN (10102216)
PARTH KHANDURI (10102171)

CERTIFICATE
This is to certify that the work titled “EDGE DETECTION IN COLOUR IMAGE” submitted by PARTH KHANDURI
(10102171) & SHEFALI JAIN (10102216) in partial fulfillment for the award of degree of B.Tech of Jaypee Institute of Information Technology, Noida has been carried out under my supervision. This work has not been submitted partially or wholly to any other University or Institute for the award of this or any other degree or diploma.

SIGNATURE OF SUPERVISOR:________________________
NAME OF SUPERVISOR: Mr. Abhinav Gupta
DESIGNATION:____________________________________
DATE:___________________________________________

ACKNOWLEDGEMENT
We would like to express our deep gratitude to Mr. Abhinav Gupta sir , ECE Deptt., Jaypee Institute of Information Technology University, Noida who assigned us the project and guided us throughout the project preparation and helped us to overcome the difficulties during the completion of the project.

NAME: PARTH KHANDURI

NAME:SHEFALI JAIN

ROLL NO: 10102171

ROLL NO: 10102216

CONTENTS
CHAPTER 1: INTRODUCTION
1.1 Background
1.2 Motivation
1.3 Outline

CHAPTER 2: COLOR SPACE
2.1 Introduction
2.2 Color Image Models
2.2.1 RGB Model
2.2.2 rgb Model

CHAPTER 3: COLOR SIMILARITY MEASURE
3.1 Introduction
3.2 Euclidean Distance
3.3 RGB

CHAPTER 4: COLOR IMAGE EDGE DETECTION
4.1 Introduction
4.2 Edge detection
4.3 Edge detection in color image

4.4 Techniques used

CHAPTER 5: DIFFERENTIAL OPERATORS
5.1 Sobel operator
5.2 Formulation
5.3 Thresholding and linking

CHAPTER 6: VECTOR ORDER STATISTIC OPERATOR
6.1 Introduction
6.2 Edge detectors
6.2.1 Vector range edge detector
6.2.2 Minimum vector range edge detector
6.2.3 Vector dispersion edge detector
6.2.4 Minimum vector dispersion edge detector

CHAPTER 7: PROJECT IMPLEMENTATION
7.1 Input image
7.2 On applying sobel operator
7.3 On applying VR edge detector
7.4 On applying MVR edge detector
7.5 On applying VD edge detector
7.6 On applying MVD edge detector

CHAPTER 8: RESULTS AND CONCLUSIONS
8.1 Results
8.2 Conclusion

APPENDIX A
APPENDIX B
APPENDIX C
APPENDIX D
APPENDIX E
APPENDIX F
REFRENCES

CHAPTER 1
INTRODUCTION
1.1 Introduction
When humans are asked to describe a picture, they generally give a list of objects within the picture as well as their relative positions. However, upon closer examination the image reveals object shadows, highlights from shiny object parts and differences in the color brightness of an. Shiny object parts could also produce object reflections in another object. Various physical processes by which light interacts with matter can explain these optical effects. An image understanding system that would generate descriptions similar in quality to the ones given by humans will have to discount the influence of these physical processes. The purpose of a general image understanding system is to recognize objects in a complex scene or document image. Typically, one of the first steps in such a system is edge detection. Edge detection algorithms usually detect sharp transitions of intensity and/or color within an image. These transitions are characteristic of object edges. Once edges of an object are detected other processing such as region segmentation, text finding, and object recognition can take place. Researchers have concentrated in the past few decades on devising algorithms for grayscale image understanding. With the advent of powerful personal computers, it is now possible and practical to move to the more computationally intensive realm of color image understanding. There are many benefits in using color images. For example, the increase in the quantity of information can be used for more...
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