Bayesian Modeling of Uncertainty in Low-Level Vision, Richard Szeliski

Автор: Osher Stanley, Paragios Nikos
Название: Geometric Level Set Methods in Imaging, Vision, and Graphics
ISBN: 0387954880 ISBN-13(EAN): 9780387954882
Издательство: Springer
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Цена: 16769.00 р.
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Описание: The topic of level sets is currently very timely and useful for creating realistic 3-D images and animations. They are powerful numerical techniques for analyzing and computing interface motion in a host of application settings. In computer vision, it has been applied to stereo and segmentation, whereas in graphics it has been applied to the postproduction process of in-painting and 3-D model construction.Osher is co-inventor of the Level Set Methods, a pioneering framework introduced jointly with James Sethian from the University of Berkeley in 1998. This methodology has been used up to now to provide solutions to a wide application range not limited to image processing, computer vision, robotics, fluid mechanics, crystallography, lithography, and computer graphics.The topic is of great interest to advanced students, professors, and R&D professionals working in the areas of graphics (post-production), video-based surveillance, visual inspection, augmented reality, document image processing, and medical image processing. These techniques are already employed to provide solutions and products in the industry (Cognitech, Siemens, Philips, Focus Imaging). An essential compilation of survey chapters from the leading researchers in the field, emphasizing the applications of the methods. This book can be suitable for a short professional course related with the processing of visual information.

Автор: Yun Fu
Название: Low-Rank and Sparse Modeling for Visual Analysis
ISBN: 3319119990 ISBN-13(EAN): 9783319119991
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data.

Автор: Yun Fu
Название: Low-Rank and Sparse Modeling for Visual Analysis
ISBN: 3319355678 ISBN-13(EAN): 9783319355672
Издательство: Springer
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Цена: 11878.00 р.
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Описание: This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data.

Автор: Richard Szeliski
Название: Bayesian Modeling of Uncertainty in Low-Level Vision
ISBN: 1461289041 ISBN-13(EAN): 9781461289043
Издательство: Springer
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Цена: 20956.00 р.
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Описание: 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.