A Probabilistic Approach to Geometric Hashing using Line Features

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CHAPTER 2. PRIOR AND RELATED WORK 15 is avoided. Barrow and Tenenbaum ë6ë proposes use of a hierarchical graph-searching technique which decomposes the model into independent components. 2.1.6 Evidential Reasoning Evidential reasoning is a method for combining information from diæerent sources of evidence to update probabilistic expectations. The attractive feature of using evidential reasoning for computer vision is that it allows us to combine information of varying reliability from many sources, even though no particular item of evidence is by itself strong enough for recognizing a particular object. For example, SCERPO by Lowe ë40ë is a search-based matching system. As to the search space of SCERPO, two major components are involved: è1è the space of possible viewpoints; è2è the space of selecting one model from the model database. Lowe tackles the ærst component by perceptual organization and suggests that the second component be treated by a technique of evidential reasoning. Each piece of evidence contributes to the presence of a certain object or objects, which quantitatively can be described by a probabilistic expectation. The combining of evidences thus can be quantitatively described by combining probabilistic expectations. The probability ranking is constantly updated to reæect new evidence found. For example, as soon as one object is recognized, it provides contextual information which updates the rankings and aids in the search process. Ranking and updating may take time; however, as the list of possible objects increases, cost of the evidential reasoning may be amortized and its use becomes more important. To sum up, evidential reasoning may deserve serious consideration to be incorporated into a system under development. It shows promise for carrying out the objective of combining many sources of information, including color, texture, shape and prior knowledge in a æexible way toachieve recognition. 2.1.7 Miscellaneous Dual Models If partial visibility problem is to be attacked, a recognition system using global features alone can not be feasible. Only local features can be used in the matching procedure in order to cope with overlapping parts and occlusions. One is free to choose diæerent local
CHAPTER 2. PRIOR AND RELATED WORK 16 features such aspoints, lines, curves, etc., or their combinations as the matching features, according to the nature of the objects in the model base. However, two factors of representation, completeness and eæciency, have to be taken into consideration. Completeness of representation suggests rich enough features in order to enable discrimination between similar objects; while eæciency suggests using some minimal characteristic information to match models again the scene èso that the inherent complexity of the matching algorithm will be smallè. A main drawback of representation by local features can be its incompleteness, since objects usually can not be fully described by a set of local features alone. One way to cope with this problem is to use both global and local feature representations: Local features are used in the matching procedure; while the additional complete representation is used, after the matching process, for veriæcation and further reænement of the object localization ë4,10,19,31,36ë. Dual Scenes Object recognition algorithms usually fall into two categories í those using intensity images and those using range data. However, we may use both of them as long as it helps. For example, Kishon ë34ë combines the use of both range and intensity data to extract 3-D curves from a scene. These curves will be of a much higher quality than if they were extracted from the range data alone. The combined information is also used to classify the 3-D curves as either shadow, occluding, fold or painted curves. Abstracted Features An abstracted feature is a feature which does not physically exist, but is inferred. For example, in preparing a model representation for polygonal objects, we may extend nonneighboring edges to obtain their intersection and use the contained angle as a feature. Some more advanced applications of using abstracted features include the footprint of concavity ë37ë, the Fourier Descriptor used for silhouettes description of aircraft in ë2ë and the representation of turning angle as a function of arc length used in ë3,19,49ë.
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
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Page 33 and 34: 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 73 and 74: CHAPTER 5. THE EFFECT OF NOISE ON T
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CHAPTER 5. THE EFFECT OF NOISE ON T
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Chapter 6 Bayesian Line Invariant M
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CHAPTER 6. BAYESIAN LINE INVARIANT
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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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