A Probabilistic Approach to Geometric Hashing using Line Features

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CHAPTER 3. NOISE IN THE HOUGH TRANSFORM 29 èaè A test image with serious noise. It contains roughly 40 segments. èbè 50 lines are detected using the standard Hough transform. ècè 50 lines are detected using our improved Hough transform without proximity grouping. èdè 50 lines are detected using our improved Hough transform with proximity grouping. Figure 3.2: A comparison of the results of the standard Hough technique and our improved Hough technique, when applied to a noisy image.
CHAPTER 3. NOISE IN THE HOUGH TRANSFORM 30 precisely on a digitized line except when the line has orientation of 0 æ ,45 æ ,90 æ or 135 æ è. Similarly smaller values of ær give better precision but result in the spreading of peaks. We have performed a series of experiments on synthesized images containing polygonal objects, which are arbitrarily chosen from our model base èsee Figure 3.3è. èThe same images are used in our later experiments.è model-0 model-1 model-2 model-3 model-4 model-5 model-6 model-7 model-8 model-9 model-10 model-11 model-12 model-13 model-14 model-15 model-16 model-17 model-18 model-19 Figure 3.3: The twenty models used in our experiments. From left to right, top to bottom are model-0 to model-19. Two kinds of noise are imposed in our experiments: positive and negative.
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 37: 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
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Chapter 7 The Experiments In this c
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CHAPTER 7. THE EXPERIMENTS 82 j det
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CHAPTER 7. THE EXPERIMENTS 84 Speci
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CHAPTER 7. THE EXPERIMENTS 86 Figur
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CHAPTER 7. THE EXPERIMENTS 88 èaè
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CHAPTER 7. THE EXPERIMENTS 90 èaè
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CHAPTER 7. THE EXPERIMENTS 92 èeè
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CHAPTER 7. THE EXPERIMENTS 94 of li
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CHAPTER 8. CONCLUSIONS 96 We have a
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Appendix A Given a correspondence b
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Appendix B The components of the ma
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Bibliography ë1ë A. Aho, J. Hopcr
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BIBLIOGRAPHY 104 ë23ë W. E. L. Gr
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BIBLIOGRAPHY 106 ë47ë I. Rigoutso
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A Probabilistic Approach to Geometric Hashing using Line Features

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