Perceptual Organization for Artificial Vision Systems / Edition 1

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Overview

Perceptual Organization for Artificial Vision Systems is an edited collection of invited contributions based on papers presented at The Workshop on Perceptual Organization in Computer Vision, held in Corfu, Greece, in September 1999. The theme of the workshop was 'Assessing the State of the Community and Charting New Research Directions.'
Perceptual organization can be defined as the ability to impose structural regularity on sensory data, so as to group sensory primitives arising from a common underlying cause. This book explores new models, theories, and algorithms for perceptual organization.
Perceptual Organization for Artificial Vision Systems includes contributions by the world's leading researchers in the field. It explores new models, theories, and algorithms for perceptual organization, as well as demonstrates the means for bringing research results and theoretical principles to fruition in the construction of computer vision systems. The focus of this collection is on the design of artificial vision systems. The chapters comprise contributions from researchers in both computer vision and human vision.

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Product Details

Table of Contents

Contributing Authors. 1. Introduction; K.L. Boyer, S. Sarkar. Part I: Focused Deliberations. 2. Principles and Methods; D. Jacobs, et al. 3. Learning and Perceptual Organization; E. Saund, et al. 4. Spatiotemporal Grouping; K.L. Boyer, et al. Part II: Discourses in Human and Machine Vision. 5. Gestalt: From Phenomena to Laws; M. Kubovy, S. Gepshtein. 6. Convexity in Perceptual Completion; Z. Liu, et al. 7. A Gestalt Model of Spatial Perception; S. Lehar. 8. What Makes Viewpoint Invariant Properties Perceptually Salient? D. Jacobs. 9. Contour and Texture Analysis for Image Segmentation; J. Malik, et al. 10. Perceptual Organization for Generic Object Description; R. Nevatia. 11. Toward Richer Labels for Visual Structure; E. Saund. 12. Tensor Voting; C.-K. Tang, et al. 13. An observation on saliency; M. Lindenbaum, A. Berengolts. 14. Closed Curves in the Analysis and Segmentation of Images; K.K. Thornberg, L.R. Williams. 15. The curve indicator random field: Curve organization via edge correlation; J. August, S.W. Zucker. 16. Euler Spiral for Shape Completion; B.B. Kimia, et al. 17. Bayesian Extraction of Collinear Segment Chains from Digital Images; D. Crevier. 18. Object Detection by Multiprimitive Preattentive Perceptual Organization; P. Vasseur, et al. Index.

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