A Probabilistic Approach to Geometric Hashing using Line Features

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Chapter 1 Introduction One of the most important goals of computer vision research is object recognition. This humanlike visual capability would enable machines to sense and analyze their environment and to take an appropriate action as desirable. We consider intensity-image techniques. Range data is usually harder to obtain. There is also reason to believe that human vision emphasizes intensity images and that most practical applications of computer vision can be tackled without range information ë42ë. Speciæcally, we consider the problem of 2-D èor, æat 3-Dè object recognition under various viewing transformations. We are interested in è1è large model bases èmore than ten objectsè; è2è cluttered scene èlow signal noise ratioè; è3è high occlusion levels; è4è segmentation diæculties. The current approaches to image segmentation generally produce incomplete boundaries and extraneous edge indications. Therefore, any approach to object recognition has to cope with segmentation defects. To work in environments containing many occlusions, we impose minimal segmentation requirements | only positional information of edgels is assumed to be available. We will describe the analysis, design and implementation of a recognition system that can recognize, in a seriously degraded intensity image, multiple objects which can be modeled as collections of lines. 1
CHAPTER 1. INTRODUCTION 2 1.1 Model-Based Object Recognition Recognition can be achieved by establishing correspondences between many kinds of predicted and measured object properties, including shape, color, texture, connectivity, context, motion, or shading. Here, we will focus upon the problem of achieving spatial correspondence. This is often prerequisite to examining correspondences along the other dimensions. Prior research ë7,13ë has indicated that a model-based approach to object recognition can be very eæective inovercoming occlusion, complication and inadequate or erroneous low level processing. Most commercial vision systems are model-based ones, in which recognition involves matching an input image to a set of predeæned models of objects. A key goal in this approach is to precompile a description of a known set of objects, then to use these object models to recognize in an image each instance of an object and to specify its position and orientation relative to the viewer. 1.1.1 Problem Deænition Object recognition can be conceptualized in various ways. A brief survey of the literature on this subject demonstrates this point ë7,8,13ë. Here, we adopt the deænition given by Besl and Jain ë7ë. Their deænition is motivated by the observation of human visual capabilities. Two main steps are involved. The ærst step is learning. When a new object is given, human visual system gathers information about that object from many diæerent viewpoints. This process is usually referred to as model formation. The second step is identiæcation. The following deænition of the single-arbitrary-view model-based object recognition problem is motivated by the above: 1. Given any collection of labeled solid objects, èaè each object can be examined as long as the object is not deformed; èbè labeled models can be created using information from this examination. 2. Given digitized sensor data corresponding to one particular, but arbitrary æeld of view of the real world, given any data stored previously during the model formation process, and given the list of distinguishable objects, the following questions must be answered:
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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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A Probabilistic Approach to Geometric Hashing using Line Features

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