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Titlebook: Hierarchical Neural Networks for Image Interpretation; Sven Behnke Book 2003 Springer-Verlag Berlin Heidelberg 2003 cognition.computer vis

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書目名稱Hierarchical Neural Networks for Image Interpretation
編輯Sven Behnke
視頻videohttp://file.papertrans.cn/427/426143/426143.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Hierarchical Neural Networks for Image Interpretation;  Sven Behnke Book 2003 Springer-Verlag Berlin Heidelberg 2003 cognition.computer vis
描述.Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains...This book?sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. ..Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks..
出版日期Book 2003
關(guān)鍵詞cognition; computer vision; control; learning; neural architectures; neural network; neural network learni
版次1
doihttps://doi.org/10.1007/b11963
isbn_softcover978-3-540-40722-5
isbn_ebook978-3-540-45169-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2003
The information of publication is updating

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0302-9743 ed learning techniques. ..Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks..978-3-540-40722-5978-3-540-45169-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Unsupervised Learning be designed manually. If multiple layers of abstraction are needed, the design complexity explodes with height, as the number of different feature arrays and the number of potential weights per feature increase exponentially.
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