Depth From Defocus : A Real Aperture Imaging Approach - Subhasis Chaudhuri

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Depth From Defocus

A Real Aperture Imaging Approach

By: Subhasis Chaudhuri, A. Pentland (Foreword by), A.N. Rajagopalan

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Hardcover | 26 March 1999

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196 Pages

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24.13 x 15.88 x 1.91

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Computer vision is becoming increasingly important in several industrial applications such as automated inspection, robotic manipulations and autonomous vehicle guidance. These tasks are performed in a 3-D world and it is imperative to gather reliable information on the 3-D structure of the scene. This book is about passive techniques for depth recovery, where the scene is illuminated only by natural light as opposed to active methods where a special lighting device is used for scene illumination. Passive methods have a wider range of applicability and also correspond to the way humans infer 3-D structure from visual images.

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Foreword	p. vii
Preface	p. ix
Acknowledgments	p. xiii
List of Symbols	p. xix
Passive Methods for Depth Recovery	p. 1
Introduction	p. 1
Different Methods of Depth Recovery	p. 2
Depth from Stereo	p. 2
Structure from Motion	p. 4
Shape from Shading	p. 6
Range from Focus	p. 8
Depth from Defocus	p. 10
Difficulties in Passive Ranging	p. 11
Organization of the Book	p. 12
Depth Recovery from Defocused Images	p. 14
Introduction	p. 14
Theory of Depth from Defocus	p. 16
Real Aperture Imaging	p. 16
Modeling the Camera Defocus	p. 18
Depth Recovery	p. 19
Sources of Errors	p. 21
Related Work	p. 22
Summary of the Book	p. 25
Mathematical Background	p. 28
Introduction	p. 28
Time-Frequency Representation	p. 29
The Complex Spectrogram	p. 30
The Wigner Distribution	p. 31
Calculus of Variations	p. 32
Markov Random Fields and Gibbs Distributions	p. 34
Theory of MRF	p. 35
Gibbs Distribution	p. 36
Incorporating Discontinuities	p. 38
Depth Recovery with a Block Shift-Variant Blur Model	p. 40
Introduction	p. 40
The Block Shift-Variant Blur Model	p. 41
Estimation of Blur	p. 43
Special Cases	p. 43
Experimental Results	p. 44
Discussion	p. 52
Space-Variant Filtering Models for Recovering Depth	p. 55
Introduction	p. 55
Space-Variant Filtering	p. 56
Depth Recovery Using the Complex Spectrogram	p. 57
The Pseudo-Wigner Distribution for Recovery of Depth	p. 58
Imposing Smoothness Constraint	p. 59
Regularized Solution Using the Complex Spectrogram	p. 59
The Pseudo-Wigner Distribution and Regularized Solution	p. 60
Experimental Results	p. 61
Discussion	p. 66
ML Estimation of Depth and Optimal Camera Settings	p. 70
Introduction	p. 70
Image and Observation Models	p. 72
ML-Based Recovery of Depth	p. 73
Computation of the Likelihood Function	p. 75
Optimality of Camera Settings	p. 77
The Cramer-Rao Bound	p. 77
Optimality Criterion	p. 79
Experimental Results	p. 81
Discussion	p. 89
Recursive Computation of Depth from Multiple Images	p. 91
Introduction	p. 91
Blur Identification from Multiple Images	p. 92
Minimization by Steepest Descent	p. 94
Recursive Algorithm for Computing the Likelihood Function	p. 95
Single Observation	p. 97
Two Observations	p. 97
General Case of M Observations	p. 98
Experimental Results	p. 99
Discussion	p. 102
MRF Model-Based Identification of Shift-Variant PSF	p. 105
Introduction	p. 105
A MAP-MRF Approach	p. 107
The Posterior Distribution and Its Neighborhood	p. 109
MAP Estimation by Simulated Annealing	p. 112
Experimental Results	p. 113
Discussion	p. 120
Simultaneous Depth Recovery and Image Restoration	p. 122
Introduction	p. 122
Depth Recovery and Restoration using MRF Models	p. 124
Locality of the Posterior Distribution	p. 126
Parameter Estimation	p. 129
Experimental Results	p. 131
Discussion	p. 139
Conclusions	p. 141
Partial Derivatives of Various Quantities in CRB	p. 150
CRB of Var(s) for a Single Image	p. 154
References	p. 157
Index	p. 169
Table of Contents provided by Syndetics. All Rights Reserved.

Depth From Defocus

A Real Aperture Imaging Approach

At a Glance

Hardcover

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