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Bayesian Modeling of Uncertainty in Low-Level Vision

  • Book
  • © 1989

Overview

Authors:
  1. Richard Szeliski

    1. Carnegie Mellon University, USA

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xix
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  2. Introduction

    • Richard Szeliski
    Pages 1-13

  3. Representations for low-level vision

    • Richard Szeliski
    Pages 15-48

  4. Bayesian models and Markov Random Fields

    • Richard Szeliski
    Pages 49-58

  5. Prior models

    • Richard Szeliski
    Pages 59-82

  6. Sensor models

    • Richard Szeliski
    Pages 83-97

  7. Posterior estimates

    • Richard Szeliski
    Pages 99-119

  8. Incremental algorithms for depth-from-motion

    • Richard Szeliski
    Pages 121-148

  9. Conclusions

    • Richard Szeliski
    Pages 149-153

  10. Back Matter

    Pages 155-198
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Keywords

About this book

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 Carnegie Mellon University, presents a Bayesian model for representing and processing uncertainty in low­ level vision. Recently, probabilistic models have been proposed and used in vision. Sze­ liski's method has a few distinguishing features that make this monograph im­ portant and attractive. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Second, his method represents and computes explicitly not only the best estimates but also the level of uncertainty of those estimates using second order statistics, i.e., the variance and covariance. Third, the algorithms developed are computationally tractable for dense fields, such as depth maps constructed from stereo or range finder data, rather than just sparse data sets. Finally, Szeliski demonstrates successful applications of the method to several real world problems, including the generation of fractal surfaces, motion estimation without correspondence using sparse range data, and incremental depth from motion.

Authors and Affiliations

  • Carnegie Mellon University, USA

    Richard Szeliski

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