Bayesian Modeling of Uncertainty in Low-Level Vision (the Springer International Series in Engineering and Computer Science) (Hardcover)

Szeliski, Richard

ISBN 10: 0792390393 / ISBN 13: 9780792390398

Published by Springer, 1989

About this Item

Hardcover, 198 pp. Light corner bump, edge wear, else clean and binding tight.

Seller Inventory # 022469

Bibliographic Details

• Title: Bayesian Modeling of Uncertainty in Low-Level Vision (the Springer International Series in Engineering and Computer Science) (Hardcover)
• Publisher: Springer
• Publication Date: 1989
• Language: English
• ISBN 10: 0792390393
• ISBN 13: 9780792390398
• Binding: Hardcover
• Condition: Very Good
• Dust Jacket Condition: No Jacket
• Book Type: Book

About this title

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.