Bayesian Modeling of Uncertainty in Low-Level Vision (Paperback, Softcover reprint of the original 1st ed. 1989)

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."

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	The Springer International Series in Engineering and Computer Science, 79
Release date:	October 2011
First published:	1989
Authors:	Richard Szeliski
Dimensions:	235 x 155 x 12mm (L x W x T)
Format:	Paperback
Pages:	198
Edition:	Softcover reprint of the original 1st ed. 1989
ISBN-13:	978-1-4612-8904-3
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	1-4612-8904-1
Barcode:	9781461289043

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