Statistical and Geometrical Approaches to Visual Motion Analysis - International Dagstuhl Seminar, Dagstuhl Castle, July 13-18, 2008, Revised Papers (Paperback, 2009 ed.)

Motion analysis is central to both human and machine vision. It involves the interpretation of image data over time and is crucial for a range of motion tasks suchasobstacledetection,depthestimation,videoanalysis,sceneinterpretation, videocompressionandotherapplications. Motionanalysisis unsolvedbecauseit requires modeling of the complicated relationships between the observed image data and the motion of objects and motion patterns (e. g. , falling rain) in the visual scene. The Dagstuhl Seminar 08291 on Statistical and Geometrical Approaches to Visual Motion Analysis was held during July 13-18, 2008 at the International Conference and Research Center (IBFI), Schloss Dagstuhl, near Wadern in G- many. The workshop focused on critical aspects of motion analysis, including motion segmentation, the modeling of motion patterns and the di?erent te- niques used. These techniques include variationalapproaches,level set methods, probabilistic models, graph cut approaches, factorization techniques, and neural networks. All these techniques can be subsumed within statistical and geomet- cal frameworks. We further involved experts in the study of human and primate vision. Primatevisualsystemsareextremely sophisticatedat processingmotion, thus there is much to be learnt from studying them. In particular, we discussed how to relate the computational models of primate visual systems to those - veloped for machine vision. In total, 15 papers were accepted for these proceedings after the workshop. We werecarefulto ensurea high standardof qualityfor the accepted papers. All submissions were double-blind reviewed by at least two experts.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Image Processing, Computer Vision, Pattern Recognition, and Graphics, 5604
Release date:	July 2009
First published:	2009
Editors:	Daniel Cremers • Bodo Rosenhahn • Alan L. Yuille • Frank R. Schmidt
Dimensions:	235 x 155 x 18mm (L x W x T)
Format:	Paperback
Pages:	323
Edition:	2009 ed.
ISBN-13:	978-3-642-03060-4
Categories:	Books > Computing & IT > General theory of computing > General Books > Computing & IT > Computer programming > General Books > Computing & IT > Applications of computing > Pattern recognition Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > General Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Computing & IT > Applications of computing > Image processing > General
LSN:	3-642-03060-2
Barcode:	9783642030604

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