A Probabilistic Approach to Geometric Hashing using Line Features

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CHAPTER 1. INTRODUCTION 3 æ Does the object appear in the digitized sensor data ? æ If so, how many times does it occur ? æ For each occurrence, è1è determine the location in the sensor data èimageè, è2è determine the location èor translation parametersè of that object with respect to a known coordinate system èif possible with given sensorè, and è3è determine the orientation èor rotation parametersè with respect to a known coordinate system. This problem statement allows the possibility of using computers to solve it; at any rate the problem is clearly solvable by human beings. 1.1.2 Object Recognition Method In most object-recognition systems, one can distinguish æve sub-processes: image formation, feature extraction, model representation, scene analysis and hypotheses veriæcation. Image Formation This process creates images via sensors, which can be sensitive toavariety of signals like visible spectrum light, X-rays, etc. As said, we will concentrate on intensity images. Feature Extraction This process acts on the sensor data and extracts relevant features. The geometric features we are interested in might in principle be lines, segments, curvature extrema, curvature discontinuities, conics and so on. But we will use lines exclusively. Model Representation This process converts models to the quantities which will be used to recognize them. This process is closely related to the feature-extracting process, since the same features are supposed to be extracted from a scene for matching. Models based on geometric properties of an object's visible surfaces or silhouette are commonly used because they describe objects in terms of their constituent shape features. Although many other models of regions and images emphasizing gray-level properties èe.g.
CHAPTER 1. INTRODUCTION 4 texture and colorè have been proposed, solving the problem of spatial correspondence is often a prerequisite to their use. Thus, recognition of objects in a scene is always apt to involve construction of a shape description of objects from sensed data and then matching the description to stored object models. A shape representation scheme usually involves at least two components: 1. the primitive units, e.g. vertices è0-dimensionè, curves and edges è1-dimensionè, surfaces è2-dimension, e.g. plane surface, quadratic surfaceè and volumes è3-dimension, e.g. generalized cylindersè; 2. the way those primitives are combined to form the object models. Such a representation scheme must satisfy the following two criteria to be considered of good quality: 1. Near-Uniqueness: Ideally each object in the world should have a limited number of representations; otherwise, when image features are derived, there will be also many choices of conæguration for each representation and thus increase the computational complexity to match conægurations of image features to model representations. 2. Continuity: Similar objects should have similar representations and very diæerent objects should have very diæerent representations. Scene Analysis This process involves an algorithm for performing matching between models and scene descriptions. This process is most crucial in an object recognition system and is the focus of this thesis. It recovers the transformations the model objects undergo during the imageformation process. Sometimes a quality measure can be associated with each model instance detected; this measure can be used as a measure of belief for further decision making in an autonomous system. We will classify basic matching techniques and review them in chapter 2. The techniques we are interested in involve those allowing partial occlusion, viewing distortion and data perturbation. In this dissertation, we will focus on using the geometric hashing technique for matching. The geometric hashing technique is very good as a ælter capable of eliminating many candidate hypotheses as the identity of the objects ë38ë.
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Page 3 and 4: Dedication This dissertation is ded
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CHAPTER 4. INVARIANT MATCHING USING
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Chapter 5 The Eæect of Noise on th
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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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