Depth from Defocus: A Real Aperture Imaging Approach

ISBN-13: 9781461271642 / Angielski / Miękka / 2012 / 172 str.

Subhasis Chaudhuri; A. N. Rajagopalan; A. Pentland

Opis

Depth recovery is important in machine vision applications when a 3-dimensional structure must be derived from 2-dimensional images. This is an active area of research with applications ranging from industrial robotics to military imaging. This book provides the comprehensive details of the methodology, along with the complete mathematics and algorithms involved. Many new models, both deterministic and statistical, are introduced.

Spis treści

1 Passive Methods for Depth Recovery.- 1.1 Introduction.- 1.2 Different Methods of Depth Recovery.- 1.2.1 Depth from Stereo.- 1.2.2 Structure from Motion.- 1.2.3 Shape from Shading.- 1.2.4 Range from Focus.- 1.2.5 Depth from Defocus.- 1.3 Difficulties in Passive Ranging.- 1.4 Organization of the Book.- 2 Depth Recovery from Defocused Images.- 2.1 Introduction.- 2.2 Theory of Depth from Defocus.- 2.2.1 Real Aperture Imaging.- 2.2.2 Modeling the Camera Defocus.- 2.2.3 Depth Recovery.- 2.2.4 Sources of Errors.- 2.3 Related Work.- 2.4 Summary of the Book.- 3 Mathematical Background.- 3.1 Introduction.- 3.2 Time-Frequency Representation.- 3.2.1 The Complex Spectrogram.- 3.2.2 The Wigner Distribution.- 3.3 Calculus of Variations.- 3.4 Markov Random Fields and Gibbs Distributions.- 3.4.1 Theory of MRF.- 3.4.2 Gibbs Distribution.- 3.4.3 Incorporating Discontinuities.- 4 Depth Recovery with a Block Shift-Variant Blur Model.- 4.1 Introduction.- 4.2 The Block Shift-Variant Blur Model.- 4.2.1 Estimation of Blur.- 4.2.2 Special Cases.- 4.3 Experimental Results.- 4.4 Discussion.- 5 Space-Variant Filtering Models for Recovering Depth.- 5.1 Introduction.- 5.2 Space-Variant Filtering.- 5.3 Depth Recovery Using the Complex Spectrogram.- 5.4 The Pseudo-Wigner Distribution for Recovery of Depth.- 5.5 Imposing Smoothness Constraint.- 5.5.1 Regularized Solution Using the Complex Spectrogram..- 5.5.2 The Pseudo-Wigner Distribution and Regularized Solution.- 5.6 Experimental Results.- 5.7 Discussion.- 6 ML Estimation of Depth and Optimal Camera Settings.- 6.1 Introduction.- 6.2 Image and Observation Models.- 6.3 ML-Based Recovery of Depth.- 6.4 Computation of the Likelihood Function.- 6.5 Optimality of Camera Settings.- 6.5.1 The Cramér-Rao Bound.- 6.5.2 Optimality Criterion.- 6.6 Experimental Results.- 6.7 Discussion.- 7 Recursive Computation of Depth from Multiple Images.- 7.1 Introduction.- 7.2 Blur Identification from Multiple Images.- 7.3 Minimization by Steepest Descent.- 7.4 Recursive Algorithm for Computing the Likelihood Function.- 7.4.1 Single Observation.- 7.4.2 Two Observations.- 7.4.3 General Case of M Observations.- 7.5 Experimental Results.- 7.6 Discussion.- 8 MRF Model-Based Identification of Shift-Variant PSF.- 8.1 Introduction.- 8.2 A MAP-MRF Approach.- 8.3 The Posterior Distribution and Its Neighborhood.- 8.4 MAP Estimation by Simulated Annealing.- 8.5 Experimental Results.- 8.6 Discussion.- 9 Simultaneous Depth Recovery and Image Restoration.- 9.1 Introduction.- 9.2 Depth Recovery and Restoration using MRF Models.- 9.3 Locality of the Posterior Distribution.- 9.4 Parameter Estimation.- 9.5 Experimental Results.- 9.6 Discussion.- 10 Conclusions.- A Partial Derivatives of Various Quantities in CRB.- References.