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Add to basketPaperback. Condition: Very Good. Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .
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Add to basketCondition: Like New. Used - Like New. Book is new and unread but may have minor shelf wear. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
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Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2003
ISBN 10: 3540407227 ISBN 13: 9783540407225
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Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Aug 2003, 2003
ISBN 10: 3540407227 ISBN 13: 9783540407225
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Add to basketTaschenbuch. Condition: Neu. 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 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 240 pp. Englisch.
Published by Springer Berlin Heidelberg, 2003
ISBN 10: 3540407227 ISBN 13: 9783540407225
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Add to basketTaschenbuch. 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.
Published by Springer-Verlag GmbH, 2003
ISBN 10: 3540407227 ISBN 13: 9783540407225
Language: English
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Add to basketTaschenbuch. Condition: Neu. Hierarchical Neural Networks for Image Interpretation | Sven Behnke | Taschenbuch | xiii | Englisch | 2003 | Springer-Verlag GmbH | EAN 9783540407225 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2003
ISBN 10: 3540407227 ISBN 13: 9783540407225
Language: English
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Add to basketPaperback. Condition: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Published by Springer Berlin Heidelberg Aug 2003, 2003
ISBN 10: 3540407227 ISBN 13: 9783540407225
Language: English
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Add to basketTaschenbuch. 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.
Published by Springer Berlin Heidelberg, 2003
ISBN 10: 3540407227 ISBN 13: 9783540407225
Language: English
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Add to basketKartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes supplementary material: sn.pub/extrasHuman 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 unde.
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Add to basketCondition: New. Print on Demand pp. 240 Illus.
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Add to basketCondition: New. PRINT ON DEMAND pp. 240.