A Probabilistic Approach to Geometric Hashing using Line Features

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CHAPTER 3. NOISE IN THE HOUGH TRANSFORM 33 Image-1 Image-2 Image-3 Image-4 Image-5 Image-6 Image-7 Image-8 Image-9 Figure 3.5: Image-1 <strong>to</strong> Image-9 before noise is imposed. The same noise levels as for Image-0 are applied during our experiments.
CHAPTER 3. NOISE IN THE HOUGH TRANSFORM 34 image-0: **********************************************.*.***.*.*.............. image-1: ***************************************.***.****.....*.**...*.....*... image-2: *********************************************************..*..*....... image-3: **************************************.*******...***.*...*.*.......*.. image-4: ****************.*.***************..***.*..**.*.*.*............*...*.. image-5: ***************************************************.....*.*.*.**.....* image-6: ************************************************************.......... image-7: ****************************************************.****....*.*...... image-8: ****************************************************.*...*.*.*..*..... image-9: ***********************************..***********************.*......*. ç ç = 1.496886 èdegreeè or 0.026126 èradianè; ç r = 1.803655 èpixelè; ç çr = 0.244846 èdegree.pixelè or 0.004273 èradian.pixelè. æ case 2: 10è negative noise, along with 200 pieces of random dot noise and 200 pieces of segment noise image-0: ******************************.******.*.*........*..*..........*...... image-1: ***********************.*.******.********..**..*.*............*.*..... image-2: ***************************************.***.*.***.*...***.....*....... image-3: *************************************.*****.*.*..*......*.....*....... image-4: *****************.*********.****.*.*.*....**.*..**...*......*...*..... image-5: **************************************.********.**.*.....*...*....***. image-6: *********************************************.***.**.*.*..*..........* image-7: ************************************************...**...**...*..***.*. image-8: **************************************.**..***.*.*..*......*......*... image-9: ******************************.*.*..********.***.*.*..*..*.......*.**. ç ç = 1.656363 èdegreeè or 0.028909 èradianè; ç r = 2.059478 èpixelè; ç çr = 0.119053 èdegree.pixelè or 0.002078 èradian.pixelè. æ case 3: 10è negative noise, along with 400 pieces of random dot noise and 400 pieces of segment noise image-0: *****************.************..****.**......*.*.....*................ image-1: ***********************.*****..*********..**.......*...........*.....* image-2: *********************************.*.***.***.*.***..........*........*. image-3: *******************************.**..*.*..**...**.*.****..........**... image-4: ****************.*********.*....****..*.*..*..*...............**....*. image-5: *******************************.****.*.**..*.....*...*......*...*.**.* image-6: *********************************.*.*****.*.*..**..........*..*.*....* image-7: **********************************.*******.*****.**.....**.*......**.. image-8: **********************************...**.*..*.*.*......*............... image-9: ***************************...*..*.*.**.*****.*.....*.......*........* ç ç = 1.712783 èdegreeè or 0.029894 èradianè; ç r = 2.017484 èpixelè; ç çr = 0.217823 èdegree.pixelè or 0.003802 èradian.pixelè. æ case 4: 10è negative noise, along with 800 pieces of random dot noise and 800 pieces of segment noise image-0: *******************.***.***......****..*...*..........*..*.......*.... image-1: ****.**.**.**..*****.*.*..*.***.**.*....*..*.*....*..*..*............. image-2: ***********.********.*..*...***..****.***.....*..............*.......* image-3: *******.************.**.*.**.***...*....*.*..*.*...................... image-4: ************.*******....*.......*..******..*.............*....*....*.. image-5: ***************.***********.***......**..*........*............*...... image-6: ***************..*************..****..**.*..........*......*.........* image-7: **************.***.*****.*****..***********.*.*...**..*...**.*.*...*..
Page 1 and 2: A Probabilistic Approach to Geometr
Page 3 and 4: Dedication This dissertation is ded
Page 5 and 6: Abstract One of the most important
Page 7 and 8: 2.2.1 A Brief Description : : : : :
Page 9 and 10: List of Figures 3.1 A comparison of
Page 11 and 12: CHAPTER 1. INTRODUCTION 2 1.1 Model
Page 13 and 14: CHAPTER 1. INTRODUCTION 4 texture a
Page 15 and 16: CHAPTER 1. INTRODUCTION 6 reconstru
Page 17 and 18: CHAPTER 1. INTRODUCTION 8 Both synt
Page 19 and 20: CHAPTER 2. PRIOR AND RELATED WORK 1
Page 33 and 34: CHAPTER 3. NOISE IN THE HOUGH TRANS
Page 41: CHAPTER 3. NOISE IN THE HOUGH TRANS
Page 49 and 50: CHAPTER 4. INVARIANT MATCHING USING
Page 69 and 70: Chapter 5 The Eæect of Noise on th
Page 71 and 72: CHAPTER 5. THE EFFECT OF NOISE ON T
Page 83 and 84: Chapter 6 Bayesian Line Invariant M
Page 85 and 86: CHAPTER 6. BAYESIAN LINE INVARIANT
Page 87 and 88: CHAPTER 6. BAYESIAN LINE INVARIANT
Page 89 and 90: Chapter 7 The Experiments In this c
Page 91 and 92: CHAPTER 7. THE EXPERIMENTS 82 j det
Page 93 and 94:
CHAPTER 7. THE EXPERIMENTS 84 Speci
Page 95 and 96:
CHAPTER 7. THE EXPERIMENTS 86 Figur
Page 97 and 98:
CHAPTER 7. THE EXPERIMENTS 88 èaè
Page 99 and 100:
CHAPTER 7. THE EXPERIMENTS 90 èaè
Page 101 and 102:
CHAPTER 7. THE EXPERIMENTS 92 èeè
Page 103 and 104:
CHAPTER 7. THE EXPERIMENTS 94 of li
Page 105 and 106:
CHAPTER 8. CONCLUSIONS 96 We have a
Page 107 and 108:
Appendix A Given a correspondence b
Page 109 and 110:
Appendix B The components of the ma
Page 111 and 112:
Bibliography ë1ë A. Aho, J. Hopcr
Page 113 and 114:
BIBLIOGRAPHY 104 ë23ë W. E. L. Gr
Page 115:
BIBLIOGRAPHY 106 ë47ë I. Rigoutso
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A Probabilistic Approach to Geometric Hashing using Line Features

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