Bayesian Modeling of Uncertainty in Low-Level Vision: 79 (The Springer International Series in Engineering and Computer Science, 79) - Tapa dura

Szeliski, Richard

 
9780792390398: Bayesian Modeling of Uncertainty in Low-Level Vision: 79 (The Springer International Series in Engineering and Computer Science, 79)
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ISBN 10: 0792390393 ISBN 13: 9780792390398
Editorial: Springer, 1989

Sinopsis

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.

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Reseña del editor

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.

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Editorial
Springer
Año de publicación
1989
Idioma
Inglés
ISBN 10 
0792390393
ISBN 13 
9780792390398
Encuadernación
Tapa dura
Número de páginas
220
Contacto del fabricante
no disponible
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Otras ediciones populares con el mismo título

9781461289043: Bayesian Modeling of Uncertainty in Low-Level Vision: 79 (The Springer International Series in Engineering and Computer Science)

Edición Destacada

ISBN 10:  1461289041 ISBN 13:  9781461289043
Editorial: Springer, 2011
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