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Implementation of edge detection for a digital image

( Télécharger le fichier original )
par Innocent MBARUBUKEYE
KIST - AO Electronics and telecommunication engineering 2008
  

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KIGALI INSTITUTE OF SCIENCE AND TECHNOLOGY (KIST)

INSTITUT DES SCIENCES ET TECHNOLOGIE DE KIGALI

Avenue de l'armée, BP 3900 KIGALI-RWANDA

FACULTY OF ENGINEERING

DEPARTEMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING

CERTIFICATE

This is to certify that the project work entitled» Implementation of edge detection for digital image» is a record of original property work done by:

HARELIMANA Joyeuse (GS20031000),

MBARUBUKEYE Innocent (GS20031281),

NYIRANGENDAHIMANA Stephanie (GS20031373),

in the partial fulfillment of the requirement for the award of Bachelor of Science in Electrical and Electronics Engineering Department (EEE) of Kigali Institute of Science and Technology during the academic year 2007.

This dissertation fulfills the requirement of the institute regulations relating to the nature and standards of works for the award of Bachelor of Science in Electronics and Telecommunication Engineering Degree.

Date......../......../........

.................... ..... ..... .................................

Mr. RUBESH Anand Dr.Eng. NTAGWIRUMUGARA

Etienne.

Supervisor Head of Department

DECLARATION

We, HARELIMANA Joyeuse (GS20031000), MBARUBUKEYE Innocent (GS20031281), NYIRANGENDAHIMANA Stéphanie (GS20031373), declare that all the work presented in this report is original.

To the best of our knowledge, the same work has never presented in KIST or any other universities or institutions of high learning.

A reference though was done and was provided from other people's work therefore, we declare that this work is ours.

Signed by:

Joyeuse HARELIMANA .....................................

Innocent MBARUBUKEYE ..................................

Stephanie NYIRANGENDAHIMANA .......................................

DEDICATION.

To our God and our savior Jesus,

To our beloved parents,

To our brothers and sisters,

To our families and

To all our close friends

We dedicate this work.

ACKNOWLEDGEMENT

First we would like to thank the Almighty GOD for keeping us alive during our whole study.

We express our gratitude to every one who contributes in our studies, especially to our government, who gives us the opportunity of studding at high learning institution and KIST administration board who gave us financial support for this final year project.

We would like to thank all staffs of Electrical and Electronics Engineering Department in KIST and Estates Officer for their support and extra knowledge, which they gave us.

We would like to extend special thanks to our supervisor Mr. RUBESH Anand for giving up many ideas and essential hint to support my research to be conducted.

We express our deep gratitude to Mr. Kanyarubira J.Baptiste, Mr. Nsekanabo Emmanuel and Mr. Nyirimihigo Célestin for their greater support without them we will not finish this work.

We are especially grateful to our parents whose patience; encouragement and understanding were vital to the completion of this study.

Once again it is pleasure to acknowledge all people who support us directly and indirectly in completion of this project.

We wish them all the best.

ABSTRACT

The world is growing up in information technology where the processing of images is required also. From the past until now, human being is seeking how to improve knowledge and updating system from oldest to the latest ones.

As memorial, in past, human used scripts and drawing for keeping images to be used in future but that oldest technology was so poor because it took along time to achieve it.

After that, analog pictures and image processing came on, whereby analog cameras were used. This was so fast and doest not require energy and forces for processing it.

But the worst of this analog technology is the storage of this image and no further processing which can be carried out on this. As solution, the digital image processing is widely used for overcoming a problem of storing and further processing that has been observed in analog image processing.

In digital image processing, different technologies along with computer are used for better image processing. This work of digital image processing, the focus is on Edge detection for digital images.

In the oldest research, the different technologies such as photometric and geometric models are used whereby some failures were experienced.

During our work, these failures and difficulties are overcoming by using MATLAB software where the edges of image are well and easier detected.

LIST OF FIGURES AND TABLES.

Figure 2.1: Fundamental steps in digital image processing..........................................6

Table 2..4.1: shows the two Roberts Cross kernels....................................................18

Table 2..4.2: shows the sobel kernel...........................................................................20

Table 2..4.3: shows Prewitt's mask............................................................................22

Table 3.1.a: shows product and comand used............................................................35

Table 3.1.b: shows Excel files to use with each Excel version..................................35

Table 3.1.c: shows finding information about matlab...............................................35

Table 3.1.d: shows conversion between different formats........................................47

Table 3.1: conversion between image types.............................................................48

Table 4.1: shws comparison of number of pixels between PNG, GIF, BMP file formats and JPEG file format.....................................................................................62

Table 4.2: shows the results based on variation of contrast and brightness...............64

NOMENCLATURE LIST.

BMP: Bitmap file format

CCD: charge couple device

e.g.: example given

eq.: equation

Fig.: figure

HDF: Hierarchical Data Format

I/O: Input/Output

IEEE: Institute of Electrical and Electronics Engineers

JPEG: Joint Photography Experts Group

KIST: Kigali Institute of Science and Technology

LCD: Liquid Crystal Display

LUT: Look Up Table

RGB: Red Green and Blue

SI: System International

SLR: Single Lens Reflex

TIFF: Tagged Image File Format

VDT: Video Display Terminal

i.e.: id est

LZW: Lempel-Ziv-Welch

DSC: Digital Still Cameras

CMOS: Complementary Metal Oxide Semiconductors

DC: Direct Current

DSLRs: Digital Single-Lens Reflex cameras

PNG: Portable Network Graphics

GIF: Graphic Interchange Format

TABLE OF CONTENTS

CERTIFICATE i

DECLARATION ii

DEDICATION. iii

ACKNOWLEDGEMENT iv

ABSTRACT v

LIST OF FIGURES AND TABLES. vi

NOMENCLATURE LIST. vii

TABLE OF CONTENTS viii

CHAPTER 1. INTRODUCTION 1

1.1. GENERAL INTRODUCTION. 1

1.2 PROBLEM IDENTIFICATION. 2

1.3. SCOPE OF THE STUDY. 2

1.4. TECHNIQUES USED. 2

1.5. OBJECTIVES OF THE PROJECT. 2

1.6. PROJECT OUTLINE. 2

CHAPTER 2. LITERATURE REVIEW. 4

2.1 INTRODUCTION TO THE IMAGE. 4

2.1.0. Digital image. 4

2.1.1. Digital image processing 4

2.1.2. Fundamental steps in digital image processing 6

2.1.3. Image segmentation 8

2.1.4. Image representation 8

2.2 DIGITAL COMPUTER 9

2.2.1. Definition of digital computer 9

2.2.2. Working of computers. 9

2.3. INTRODUCTION TO CAMERAS. 9

2.3.0. Film cameras 9

2.3.1. Digital cameras 10

2.4. EDGE DETECTION 14

2.4.1 Introduction to the edge detection 14

2.4.2 Edge properties. 16

2.4.3 Detecting an edge. 16

2.4.4 Edge detection algorithms. 17

2.5. PIXELS 22

2.6. PRIMARY COLOURS 25

2.7. MULTI-SPECTRAL IMAGES 26

2.8. LOOK UP TABLES AND COLOURMAPS 27

2.9. LUMINOUS INTENSITY 28

2.10. GAUSSIAN SMOOTHING 30

2.11. FACE IDENTIFICATION. 31

CHAPTER 3. RESEARCH METHODOLOGY. 32

3.1. METHOD USED AND SOFTWARE DEVELOPMENT. 32

3.1.1. Definition of matlab. 32

3.1.3. Function used by matlab. 35

3.1.4. Commands used. 38

3.1.5. Symboles used. 38

3.1.6. To write matlab programs 39

3.1.7. To end a program. 41

3.1.8. Access to implementation details 41

3.1.9. Fundamentals on matlab. 42

3.1.10. Image types and data classes 43

3.1.11. Conversion between different formats 46

3.2. EQUIPMENTS USED. 47

3.3. MATERIALS USED. 47

3.4. DETECTING AN OBJECT USING IMAGE SEGMENTATION. 47

3.5. IMPLEMENTATION OF EDGE DETECTION. 50

CHAPTER 4. RESULTS AND DISCUSSION. 60

4.1. RESULTS. 60

4.2. DISCUSSION. 63

CHAPTER 5. CONCLUSION AND RECOMMENDATION. 64

5.1. CONCLUSION. 64

5.2. RECOMMENDATION. 64

REFERENCES. 65

APPENDICES 66

CHAPTER 1. INTRODUCTION

1.1. GENERAL INTRODUCTION.

Edge detection in the human brain takes place automatically. It is an important concept both in the area of image processing and in the area of object recognition, Without being able to determine where the edge of an object fall, a machine would be able unable to determine many things about that object such as shape, volume, and area. Being able to recognize an object is a key step towards the development of artificial intelligence.

Edge detection is the process of localizing pixel intensity transitions. The edge detection have been used by object recognition, target tracking, segmentation, and etc. Therefore, the edge detection is one of the most important parts of image processing.

Edge detection can be used for numerous applications in which access to an article is to be restricted to a limited number of persons.

The edge detection offers several benefits including simplicity, convenience, security and accuracy.

The edge detection is an important in most image processing techniques. It can identify the area of image where large change in intensity occurs.

These changes are associated with the physical boundary or edge in the scene from which the image is derived.

There are many techniques used in edge detection, but most of them can be grouped into two categories:

1. Search based and

2. Zero crossing based.

The search-based methods detect edges by looking for maxima and minima in the first derivative of an image.

The zero crossing based methods: search for zero crossing in the 2nd derivative of the image in the order to find an image.

The area where images are processed using proprietary software, for example Photoshop where the implementation details of any image processing algorithm are inaccessible. Some algorithms are quite complex and the result may be sensible to subtleties in the implementation. An image that has been extensively processed using proprietary software may well be challenged in court. This problem is solved by using MATLAB software which may not be as user friendly as an application like Photoshop; however, being a general purpose programming language it provides many important advantages for forensic image processing.

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