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9780792377993

Perceptual Organization for Artificial Vision Systems

by Boyer, Kim L.; Sarkar, Sudeep
  • ISBN13:

    9780792377993

  • ISBN10:

    0792377990

  • eBook ISBN(s):

    9781461544135

  • Format: Hardcover
  • Copyright: 2000-03-01
  • Publisher: Kluwer Academic Pub
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Summary

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.

Table of Contents

Contributing Authors xi
Introduction
1(16)
Kim L. Boyer
Sudeep Sarkar
Introduction
1(3)
The Breakout Reports
4(3)
A Snapshot: Issues from the Floor
7(2)
Research Contributions: Perceptual Psychology meets Computer Vision
9(2)
Conclusions and Recommendations
11(6)
Part I Focused Deliberations
Principles and Methods
17(12)
David Jacobs
Jitendra Malik
Ram Nevatia
Introduction
17(1)
Goals of perceptual organization
18(2)
State of the art
20(2)
Future directions
22(7)
Learning and Perceptual Organization
29(4)
Eric Saund
Jonas August
Joachim Buhmann
Daniel Crevier
Greet Frederix
Danny Roobaert
Introduction
29(1)
What is there to learn in PO?
30(1)
Common Perceptual Organization Engine
30(1)
Training Data
31(1)
Why is learning important?
32(1)
Spatiotemporal Grouping
33(8)
Kim L. Boyer
Daniel Fagerstrom
Michael Kubovy
Peter Johansen
Sudeep Sarkar
Introduction
33(1)
Three Basic Paradigms
34(2)
The Questions
36(1)
Conclusions and Recommendations
37(4)
Part II Discourses in Human and Machine Vision
Gestalt: From Phenomena to Laws
41(32)
Michael Kubovy
Sergei Gepshtein
Introduction
41(2)
Grouping by Proximity in Space
43(6)
Grouping by Proximity and Similarity
49(5)
Grouping by Proximity in Space-Time
54(19)
Convexity in Perceptual Completion
73(18)
Zili Liu
David W. Jacobs
Ronen Basri
Introduction
73(1)
Computational theories
74(1)
Psychological theories
75(1)
The convexity theory
75(2)
Grouping and depth discrimination
77(2)
Experiment 1:
79(4)
Experiment 2
83(4)
Discussion
87(4)
A Gestalt Model of Spatial Perception
91(30)
Steven Lehar
Introduction
92(2)
The Gestalt Properties of Perception
94(5)
The Computational Mechanism of Perception
99(2)
A Gestalt Bubble Model
101(14)
Brain Anchoring
115(1)
Conclusion
116(5)
What Makes Viewpoint Invariant Properties Perceptually Salient?
121(18)
David Jacobs
Introduction
121(1)
Viewpoint Invariance in Computational Grouping
122(1)
Viewpoint Invariance in Points
123(8)
Other Gestalt properties
131(1)
Discussion: Why Minimal Features?
132(7)
Contour and Texture Analysis for Image Segmentation
139(34)
Jitendra Malik
Serge Belongie
Thomas Leung
Jianbo Shi
Introduction
139(6)
Filters, Composite Edgels, and Textons
145(8)
The Normalized Cut Framework
153(1)
Defining the Weights
154(7)
Computing the Segmentation
161(4)
Results
165(8)
Perceptual Organization for Generic Object Description
173(18)
R. Nevatia
Introduction
173(1)
The Role of Perceptual Organization
174(2)
Saliency of Features
176(1)
An Approach to Perceptual Organization
177(2)
Some System Realizations
179(6)
Combining Evidence, Uncertainty Reasoning and Machine Learning
185(2)
Conclusions
187(4)
Toward Richer Labels for Visual Structure
191(24)
Eric Saund
Introduction
191(1)
The Strength of Weak Models
192(2)
Perceptual Organization in Document Images
194(7)
Perceptual Organization in Posterized Scenes
201(9)
Conclusion
210(5)
Tensor Voting
215(24)
Chi-Keung Tang
Mi-Suen Lee
Gerard Medioni
Introduction
215(1)
Previous work
216(2)
Salient inference engine overview
218(1)
Tensor representation
219(3)
Tensor communication
222(3)
Feature extraction
225(2)
Complexity
227(1)
Results in 2-D
227(3)
Results in 3-D
230(4)
Conclusion
234(1)
Software Systems
235(4)
An observation on saliency
239(10)
Michael Lindenbaum
Alexander Berengolts
Introduction
239(2)
Probabilistic Saliency
241(1)
The probabilistic saliency optimization process
242(3)
Implementation
245(1)
Conclusion
246(3)
Closed Curves in the Analysis and Segmentation of Images
249(16)
K. K. Thornber
L. R. Williams
Motivation
249(1)
Theory
250(8)
Results
258(4)
Conclusion
262(3)
The curve indicator random field: Curve organization via edge correlation
265(24)
Jonas August
Steven W. Zucker
Introduction
265(3)
Overview of Our Probabilistic Model for Curve Organization
268(1)
The Underlying Curve Field Model
268(4)
The Oriented Wiener Filter
272(4)
Validating the Edge Correlation Assumption
276(10)
Summary
286(3)
Euler Spiral for Shape Completion
289(22)
Benjamin B. Kimia
Ilana Frankel
Ana-Maria Popescu
Introduction
290(6)
Euler's Spiral
296(3)
Euler's Spiral for Boundary Modeling and Gap Completion
299(1)
Biarc Construction and Interpolation
300(4)
Examples
304(1)
Summary and Discussion
305(6)
Bayesian Extraction of Collinear Segment Chains from Digital Images
311(14)
Daniel Crevier
Introduction
311(2)
Edge Detection and Linking
313(1)
Deviation Measures
313(1)
Underlying Accidental Distributions
314(1)
Prior Accidental Densities of Deviation Measures
315(1)
Extraction of the Prior Probability of Non-accidental Junctions
316(1)
Extraction of Non Accidental Junctions
317(1)
Extraction of Candidate Chains
317(1)
Validation of Chains
318(1)
Iterative Procedure
319(1)
Examples and Conclusion
319(6)
Object Detection by Multiprimitive Preattentive Perceptual Organization
325(22)
Pascal Vasseur
El Mustapha Mouaddib
Claude Pegard
Arnaud Dupuis
Introduction
326(2)
Previous Work
328(2)
The Multi-primitive Pre-attentive Approach
330(9)
Experimental Results
339(4)
Conclusion
343(4)
Index 347

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