COLOR TRANSFORMATION FOR PROTANOPIA COLOR VISION DEFICIENCY USING INTEGRATION OF IMAGE PROCESSING AND ARTIFICIAL NEURAL NETWORK NUR HIDAYATUL NADIHAH BINTI ABD WAHAB A project report submitted in partial fulfilment of the requirements for the award of the degree of Master of Engineering (Mechatronics & Automatic Control) Faculty of Electrical Engineering Universiti Tecknologi Malaysia DECEMBER 2015 iii DEDICATION Dedicated to my beloved family, lecturers and friends, who make all things seem possible For their encouragement, support and motivation through my education journey iv ACKNOWLEDGEMENT I am indeed grateful to Almighty Allah for providing me opportunity and strength to commence and conclude my Master of Engineering at University Teknologi Malaysia I would like to express my heart-felt gratitude to my supervisor Dr. Fatimah Sham Binti Ismail for her inspirational guidance and excellent supervision and encouragement throughout the duration of my work. I would also like to express my gratitude to my family who has been giving a lot of morale support to me. Thanks for their encouragement, love, emotional and financial support that they had given to me. My great appreciation dedicated to my friends and course mates and those whom involve directly or indirectly with this project. Thank You So Much. v ABSTRACT Color blindness deficiency is inability to distinguish colors with each other. Nowadays, the individual who are not being able to recognize color may be crucial in some day life situation because many common activities depend on signals with color-coded such as road sign, traffic light, electric wire, resistor and many more. There are many forms of color blindness such Monochromacy (total color blindness), Dichromacy (Red/ Green/Blue blindness) and Trichromacy and etc. Most types of defective color blindness can be classified into two categories which are green color defective and red color defective. The objective of this project is to improve the ability of color discrimination for Protanopia which a type of dichromacy where the patients does not naturally develop red color or Long wavelength cones in their eyes. This project proposed a method using image processing to improve the ability of color discrimination for Protanopia as well as adjusting images such that a person suffering from Protanopia is able perceive image detail and color dynamics. This method is first developed by simulating an image through the eyes of a person suffering from protanopia by converting RGB space to LMS (long, medium, short) color space based on cone response and then modifies the response of the deficient cones. The linear multiplication matrix is derived by referring to CIE color matching functions. ANN is then set up by using the input/output from matrix conversion. For this research, the ANN is introduced to reduce simulation time in image processing. The transformation technique used is RGB Color Contrasting where this step is to enhance contrast between red and green which in general, make green pixels appear to be bluer. Based on the result, the objectives are successfully achieved. ANN gives the minimum computational time than conventional matrix conversion which is 36% increment. The changes of the image drastically for both color blind and non-color blind viewers. The result shows that the reds become redder and greens become greener from the image before being adjusted. vi ABSTRAK Buta warna ialah ketidakmampuan individu untuk membezakan warna antara satu sama lain. Pada masa kini, individu yang menghidapi penyakit buta warna mungkin mengalami masalah dalam beberapa kehidupan seharian kerana banyak aktiviti bergantung kepada isyarat yang menggunakan kod warna seperti tanda jalan, lampu isyarat, wayar elektrik, perintang dan pelbagai lagi. Terdapat banyak jenis warna buta seperti Monochromacy (buta warna), Dichromacy (merah / hijau / buta biru) dan Trichromacy dan lain-lain. Kebanyakan jenis buta warna boleh diklasifikasikan kepada dua kategori iaitu kerosakan warna hijau dan kerosakan warna merah. Objektif projek ini adalah untuk meningkatkan keupayaan diskriminasi warna untuk Protanopia (dichromacy) yang mana pesakit secara semula jadi tidak dapat melihat warna merah atau gelombang kon panjang di mata mereka. Projek ini mencadangkan satu kaedah menggunakan pemprosesan imej untuk meningkatkan keupayaan diskriminasi warna untuk Protanopia serta menyesuaikan imej dengan orang yang mengalami Protanopia supaya dapat melihat dengan lebih detail imej dan warna. Kaedah ini dimulakan dengan simulasi imej melalui mata seseorang yang mengalami protanopia dengan menukar ruang RGB ke ruang LMS (panjang, sederhana, pendek) warna. Pendaraban secara matriks secara linear diperolehi dengan merujuk kepada fungsi CIE yang hampir sama warna. ANN kemudiannya ditubuhkan dengan menggunakan input dan output daripada penukaran matrik. Untuk kajian ini, ANN diperkenalkan untuk mengurangkan masa simulasi dalam pemprosesan imej. Teknik transformasi yang digunakan adalah RGB warna yang berbeza di mana langkah ini adalah untuk meningkatkan kontras antara merah dan hijau yang secara umum, membuat piksel hijau kelihatan lebih biru. Perubahan imej secara drastik untuk kedua-dua buta warna dan bukan warna-penonton buta. Hasil kajian menunjukkan objektif berjaya dicapai. Kaedah ANN menunjukan masa simulasi adalah minimum berbanding kaedah penukaran matrik iaitu sebanyak 36% peningkatan. Transformasi image juga berjaya dari merah menjadi lebih merah dan warna hijau menjadi lebih hijau daripada imej sebelum diselaraskan. vii TABLE OF CONTENTS CHAPTER TITLE PAGE DECLARATION ii DEDICATION iii ACKNOWLEDGEMENT iv ABSTACT v ABSTRAK vi TABLE OF CONTENT vii LIST OF TABLES ix LIST OF FIGURES x LIST OF ABBREVIATIONS xii LIST OF APPENDICES xiii 1 INTRODUCTION 1.1 Introduction 1 1.2 Problem Statement 4 1.3 Objectives of the Project 5 1.4 Scope of Work 5 1.5 Significant of the Project 6 1.6 Thesis Organization 7 2 LITERATURE REVIEW 2.1 Introduction 8 2.2 Color Vision Deficiency (CVD) 8 2.3 Human Color Vision 9 viii 2.4 Causes of Color Vision Deficiency (CVD) 11 2.4.1 Abnormal Vision (Color Blindness) 15 2.4.2 Disorder Human Color Vision 15 2.5 Color Image Processing 16 2.5.1 Color Model and Color Space 17 RGB 2.5.2 LMS Color Space 20 2.5.3 Gamma and Linearity 20 2.5.4 CIE System 21 2.5.5 CIE Lab 22 2.5.6 Color Gamut 22 2.5.7 Previous Work 23 2.6 Neural Network 24 2.6.1 Multi-Layer Perceptron 25 2.6.2 Neural Network in Image Processing 26 2.6.3 Previous Work 26 2.7 Summary 27 3 METHODOLOGY 3.1 Introduction 28 3.2 Color Model Design 29 3.3 Simulation of Color Model using Matrix 31 Conversion 3.4 Simulation of Color Model using Artificial 34 Neural Network 3.5 Color Contrast Enhancement 37 3.6 Testing Overall Image Performance 38 3.6.1 Performance Four Samples of Image 39 3.6.2 Computational Time Measurement 39 using Four Size of Image 3.6.3 Performance of Transformed Image 39 with Computational Time 3.7 Summary 40 ix 4 RESULT 4.1 Introduction 41 4.2 Selection of Colors as a Model 41 4.3 Simulated Color Model in Protanope Vision 43 4.4 Evaluation using Levenberg-Marquardt 44 Algorithm in Neural Network 4.5 Color Transformation Performance 46 4.6 Performance Four Samples of image 48 4.7 Computational Time Measurement using Four 50 Size of Image 4.8 Performance of Transformed Image with 51 Computational Time 4.9 Summary 52 5 CONCLUSION 5.1 Introduction 53 5.2 Conclusion 54 5.3 Future Development 55 REFERENCES 56 Appendices A-B 59-68 x LIST OF TABLES TABLE NO TITLE PAGE 2.1 Percentage of color vision defects 13 3.1 Green Color Samples in RGB component values. 29 Green Color (perceive by protanope) Samples in 3.2 29 RGB component values Eight benchmark colors with their own RGB 4.1 41 values Images Before and After Color Contrast 4.2 46 Adjustment Value of RGB component for each color Images 4.3 47 before and after Color Contrast Adjustment. 4.4 Simulated Image Using Two Different Method 48 Computational Time Using Two Different 4.5 50 Method Computational Time Using Two Different 4.6 51 Methods for Image Transformation. xi LIST OF FIGURES FIGURE NO TITLE PAGE The human eye wavelength ranging from 400nm to 2.1 9 700nm 2.2 Anatomy of Human eye. 10 2.3 Cases of Sex-linked in Color Vision Deficiency 11 2.4 Color vision disorders and perception of colors 14 The Problem with Dichromat is the reddish and 2.5 15 greenish colors look yellow hue 2.6 RGB image model 16 2.7 Each pixel is encoded with three RGB values 17 Three-dimensional representation of space-RGB with 2.8 18 8bit per color 2.9 Structure of Artificial Neural Network 23 2.10 Example of Structure of MLP (3 layer) 24 3.1 Process sequences of project 23 3.3 Structure of an MLP 3layers 30 3.4 (a) Hyperbolic tangent function. (b) Pure line function 35 3.5 Neural Network Training Tool 35 3.6 The image color enhancement process 37 4.1 Color model with the arrangement of 14 colors 41 (a) Original Color image seen by Normal Person 4.2 (b) Simulation of the artwork as perceived by a 42 Protanope 4.3 Neural Network Overall Performance 43 4.4 Neural Network Training Regression 44 Simulated Image Using Two Different Method with 4.5 45 0.000026269 errors xii LIST OF ABBREVIATIONS CVD - Color Vision Deficiency ANN - Artificial Neural Network AI - Artificial Intelligence HSL - Hue, Light, Saturation HSV - Hue, Saturation, Value MATLAB - Mathematical Laboratory RGB - Red Green Blue CIE - International Commission on Illumination CRT - Cathod Ray Tube NTSC - Nasional Television System Commitee SMPTE - Society of Motion Picture and Television Engineers LMS - Long, Medium, Short xiii LIST OF APPENDICES APPENDIX TITLE PAGE A Samples of Color 59 B MATLAB Programming Code 63 CHAPTER 1 INTRODUCTION 1.1 Introduction Color is very important and plays significant role in our daily life.