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Titlebook: Cognitive Vision Systems; Sampling the Spectru Henrik I. Christensen,Hans-Hellmut Nagel Book 2006 Springer-Verlag Berlin Heidelberg 2006 Co

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书目名称Cognitive Vision Systems
副标题Sampling the Spectru
编辑Henrik I. Christensen,Hans-Hellmut Nagel
视频videohttp://file.papertrans.cn/230/229153/229153.mp4
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Cognitive Vision Systems; Sampling the Spectru Henrik I. Christensen,Hans-Hellmut Nagel Book 2006 Springer-Verlag Berlin Heidelberg 2006 Co
描述.During the last decade of the twentieth century, computer vision made considerable progress towards the consolidation of its fundaments, in particular regarding the treatment of geometry for the evaluation of stereo image pairs and of multi-view image recordings. Scientists thus began to look at basic computer vision solutions - irrespective of the well-perceived need to perfection these further - as components which should be explored in a larger context...This volume is a post-event proceedings volume and contains selected papers based on the presentations given, and the lively discussions that ensued, during a seminar held in Dagstuhl Castle, Germany, in October 2003. Co-sponsored by ECVision, the cognitive vision network of excellence, it was organized to further strengthen cooperation between research groups from different countries, and scientists active in related areas were invited from around the world...The 18 thoroughly revised papers presented are organized in topical sections on foundations of cognitive vision systems, recognition and categorization, learning and adaptation, representation and inference, control and systems integration, and conclusions..
出版日期Book 2006
关键词Computer Vision; autonom; classification; cognition; cognitive agents; cognitive computer vision; cognitiv
版次1
doihttps://doi.org/10.1007/11414353
isbn_softcover978-3-540-33971-7
isbn_ebook978-3-540-33972-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2006
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Organization of Architectures for Cognitive Vision Systemsof the system. It may be delayed in the form of a reconfiguration of internal models in relation to the interpreted . of the system. Or it may be to generate in a subsequent step a generalized . which will allow its intentions of actions to be communicated to some other system. As important as the p
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A System for Object Class Detectiont should be specific enough to reject false positives. We describe a method to learn class-specific feature detectors that are robust to intra-class variability. These feature detectors enable a representation that can be used to drive a subsequent process for verification. Instances of object class
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Greedy Kernel Principal Component Analysising cognitive vision..A technique allowing to approximate a huge training set is proposed. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis (PCA). In contrast to the PCA, the basis vectors o
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Integrating Video Information over Time. Example: Face Recognition from Videohave studied the effectiveness of temporal integration for recognition purposes by using the face recognition as an example study case. Face recognition is a prominent problem and has been studied more extensively than almost any other recognition problem. An observation is that face recognition wor
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