Bayesian Modeling of Uncertainty in Low-Level Vision

1. Front Matter

   Pages i-xix

   PDF

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

    PDF

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.

• Markov random field
• Optical flow
• Stereo
• algorithms
• behavior
• filtering
• fractals
• knowledge
• modeling
• sensor
• sensors
• uncertainty

• Carnegie Mellon University, USA

  Richard Szeliski

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