This book represents a summary of the research we have been conducting since the early 1990s, and describes a conceptual framework which addresses some current shortcomings, and proposes a unified approach for a broad class of problems. While the framework is defined, our research continues, and some of the elements presented here will no doubt evolve in the coming years.It is organized in eight chapters. In the Introduction chapter, we present the definition of the problems, and give an overview of the proposed approach and its implementation. In particular, we illustrate the limitations of the 2.5D sketch, and motivate the use of a representation in terms of layers instead.
In chapter 2, we review some of the relevant research in the literature. The discussion focuses on general computational approaches for early vision, and individual methods are only cited as references. Chapter 3 is the fundamental chapter, as it presents the elements of our salient feature inference engine, and their interaction. It introduced tensors as a way to represent information, tensor fields as a way to encode both constraints and results, and tensor voting as the communication scheme. Chapter 4 describes the feature extraction steps, given the computations performed by the engine described earlier. In chapter 5, we apply the generic framework to the inference of regions, curves, and junctions in 2-D. The input may take the form of 2-D points, with or without orientation. We illustrate the approach on a number of examples, both basic and advanced. In chapter 6, we apply the framework to the inference of surfaces, curves and junctions in 3-D. Here, the input consists of a set of 3-D points, with or without as associated normal or tangent direction. We show a number of illustrative examples, and also point to some applications of the approach. In chapter 7, we use our framework to tackle 3 early vision problems, shape from shading, stereo matching, and optical flow computa
Continuous relaxation labeling.
Stochastic relaxation labeling.
Characteristics of consistent labeling.
Clustering and robust methods.
Artificial neural network approach.
Novelty of our pproach. Chapter 3. The Salient Feature Inference Engine. Overview of the salient inference engine. Representation.
Tensor decomposition.Communication through tensor voting.
Representing the voting function by discrete tensor fields.
Deriving the stick, plate and ball tensor fields from the fundamental field.
The voting process.
Vote interpretation.Derivation and properties of the f
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- © Elsevier Science 2000
- 1st March 2000
- Elsevier Science
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Gérard Medioni received the Diplôme d'Ingéieur Civil from the Ecole Nationale Supérieure des Télécommunications, Paris, France, in 1977, and the M.S. and Ph.D. degrees in Computer Science from the University of Southern California, Los Angeles, in 1980 and 1983, respectively. He has been with the University of Southern California (USC) in Los Angeles, since 1983, where he is currently a Professor of Computer Science and Electrical Engineering. His research interests cover a broad spectrum of the computer vision field, and he has studied techniques for edge detection, perceptual grouping, shape description, stereo analysis, range image understanding, image to map correspondence, object recognition, and image sequence analysis. He has published over 100 papers in conference proceedings and journals.
He has served on program committees of many major vision conferences, and was program co-chairman of the IEEE Computer Vision and Pattern Recognition Conference in 1991, program co-chairman of the IEEE Symposium on Computer Vision held in Coral Gables, Florida, in November 1995, general co-chair of the IEEE Computer Vision and Pattern Recognition Conference in 1997 in Puerto Rico, and program co-chair of the International Conference on Pattern Recognition held in Brisbane, Australia, in August 1998. Dr. Medioni is associate editor of the
Department of Computer Science and Electrical Engineering, Institute for Robotics and Intelligent Systems, University of South California, Los Angeles, CA, USA