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Bayesian Modeling of Uncertainty in Low-Level Vision

Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski's Ph.D. dissertation at C...

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Bibliographic Details
Main Author: Szeliski, Richard
Format: eBook
Language:English
Published: New York, NY Springer US 1989, 1989
Edition:1st ed. 1989
Series:The Springer International Series in Engineering and Computer Science
Subjects:
Image Processing / Digital Techniques
Control, Robotics, Automation
Computer Vision
Artificial Intelligence
Computer Imaging, Vision, Pattern Recognition And Graphics
Control Engineering
Robotics
Automation
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 1 Introduction
  • 1.1 Modeling uncertainty in low-level vision
  • 1.2 Previous work
  • 1.3 Overview of results
  • 1.4 Organization
  • 2 Representations for low-level vision
  • 2.1 Visible surface representations
  • 2.2 Visible surface algorithms
  • 2.3 Multiresolution representations
  • 2.4 Discontinuities
  • 2.5 Alternative representations
  • 3 Bayesian models and Markov Random Fields
  • 3.1 Bayesian models
  • 3.2 Markov Random Fields
  • 3.3 Using probabilistic models
  • 4 Prior models
  • 4.1 Regularization and fractal priors
  • 4.2 Generating constrained fractals
  • 4.3 Relative depth representations (reprise)
  • 4.4 Mechanical vs. probabilistic models
  • 5 Sensor models
  • 5.1 Sparse data: spring models
  • 5.2 Sparse data: force field models
  • 5.3 Dense data: optical flow
  • 5.4 Dense data: image intensities
  • 6 Posterior estimates
  • 6.1 MAP estimation
  • 6.2 Uncertainty estimation
  • 6.3 Regularization parameter estimation
  • 6.4 Motion estimation without correspondence
  • 7 Incremental algorithms for depth-from-motion
  • 7.1 Kaiman filtering
  • 7.2 Incremental iconic depth-from-motion
  • 7.3 Joint modeling of depth and intensity
  • 8 Conclusions
  • 8.1 Summary
  • 8.2 Future research
  • A Finite element implementation
  • B Fourier analysis
  • B.1 Filtering behavior of regularization
  • B.2 Fourier analysis of the posterior distribution
  • B.3 Analysis of gradient descent
  • B.4 Finite element solution
  • B.5 Fourier analysis of multigrid relaxation
  • C Analysis of optical flow computation
  • D Analysis of parameter estimation
  • D.1 Computing marginal distributions
  • D.2 Bayesian estimation equations
  • D.3 Likelihood of observations
  • Table of symbols

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