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Titlebook: Robust Computer Vision; Theory and Applicati Nicu Sebe,Michael S. Lew Book 2003 Springer Science+Business Media Dordrecht 2003 Active conto

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书目名称Robust Computer Vision
副标题Theory and Applicati
编辑Nicu Sebe,Michael S. Lew
视频video
丛书名称Computational Imaging and Vision
图书封面Titlebook: Robust Computer Vision; Theory and Applicati Nicu Sebe,Michael S. Lew Book 2003 Springer Science+Business Media Dordrecht 2003 Active conto
描述.From the foreword by Thomas Huang:. ."During the past decade, researchers in computer vision have found that probabilistic machine learning methods are extremely powerful. This book describes some of these methods. In addition to the Maximum Likelihood framework, Bayesian Networks, and Hidden Markov models are also used. Three aspects are stressed: features, similarity metric, and models. Many interesting and important new results, based on research by the authors and their collaborators, are presented...Although this book contains many new results, it is written in a style that suits both experts and novices in computer vision." .
出版日期Book 2003
关键词Active contour; Bayesian network; Hidden Markov Model; Stereo; Textur; algorithms; classification; cognitio
版次1
doihttps://doi.org/10.1007/978-94-017-0295-9
isbn_softcover978-90-481-6290-1
isbn_ebook978-94-017-0295-9Series ISSN 1381-6446
issn_series 1381-6446
copyrightSpringer Science+Business Media Dordrecht 2003
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Introduction,ith no accompanying structural, administrative, or descriptive text information. The Internet, more specifically the Web, has become a common channel for the transmission of graphical information, thus moving visual information retrieval rapidly from stand-alone workstations and databases into a net
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Maximum Likelihood Framework,bust statistics including the outliers generation mechanisms. Further, we present the classical robust estimation procedure with an emphasis on Hampel’s approach [Hampel et al., 1986] based on influence functions. The maximum likelihood relation with other approaches is also investigated. We draw on
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Shape Based Retrieval,bject recognition, matching, registration, and analysis. Research in shape analysis has been motivated, in part, by studies of human visual form perception systems. Several theories of visual form are briefly mentioned here. A proper definition of shape similarity calls for the distinctions between
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Facial Expression Recognition,h little or no effort. Still, development of an automated system that accomplishes this task is rather difficult. There are several related problems: detection of an image segment as a face, extraction of the facial expression information, and classification of the expression (e.g., in emotion categ
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Color Based Retrieval,oints. Both the ground truth and the algorithm come from the work by Gevers and Smeulders [Gevers and Smeulders, 1999]. Furthermore, for both applications, we implement the quadratic perceptual similarity measure proposed by Hafner et al. [Hafner et al., 1995] and the correlogram introduced by Huang et al. [Huang et al., 1997] as benchmarks.
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