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Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science, 2766) - Softcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
Review
From the reviews:
"This booklet is the reprint of a thesis. It addresses image interpretation using a neural network architecture mimicking the human visual system. ... The exposition is divided in two parts, namely theory and applications. ... In short this thesis is very interesting, well written and easy to read." (Jean Th. Lapresté, Zentralblatt MATH, Vol. 1041 (16), 2004)
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date2003
- ISBN 10 3540407227
- ISBN 13 9783540407225
- BindingPaperback
- LanguageEnglish
- Number of pages240
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Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science, 2766)
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Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science)
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Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science)
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Hierarchical Neural Networks for Image Interpretation
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Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2003
ISBN 10: 3540407227
ISBN 13: 9783540407225
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Softcover. Condition: Très bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Edition 2003. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Slight signs of wear on the cover. Edition 2003. Ammareal gives back up to 15% of this item's net price to charity organizations. Seller Inventory # E-612-052
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Hierarchical neural networks for image interpretation.
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23 cm. original paperback. 224 pp. ills. references. index. "Lecture Notes in Computer Science". -(libr labels, library stamp, otherwise (very) good). 370g. Seller Inventory # 71746
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Hierarchical Neural Networks for Image Interpretation
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Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science, 2766)
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Hierarchical Neural Networks for Image Interpretation
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Springer Berlin Heidelberg Aug 2003, 2003
ISBN 10: 3540407227
ISBN 13: 9783540407225
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 booksets 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. 240 pp. Englisch. Seller Inventory # 9783540407225
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Hierarchical Neural Networks for Image Interpretation
Published by
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ISBN 10: 3540407227
ISBN 13: 9783540407225
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 booksets 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. Seller Inventory # 9783540407225
Quantity: 1 available