Bayesian Modeling of Uncertainty in Low-Level Vision

✍ Scribed by Richard Szeliski (auth.)

• Publisher: Springer US
• Year: 1989
• Tongue: English
• Leaves: 205
• Series: The Kluwer International in Engineering and Computer Science 79
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xix
Introduction....Pages 1-13
Representations for low-level vision....Pages 15-48
Bayesian models and Markov Random Fields....Pages 49-58
Prior models....Pages 59-82
Sensor models....Pages 83-97
Posterior estimates....Pages 99-119
Incremental algorithms for depth-from-motion....Pages 121-148
Conclusions....Pages 149-153
Back Matter....Pages 155-198

✦ Subjects

Computer Imaging, Vision, Pattern Recognition and Graphics;Control, Robotics, Mechatronics;Artificial Intelligence (incl. Robotics)