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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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Recensione
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)
Contenuti
I. Theory.- Neurobiological Background.- Related Work.- Neural Abstraction Pyramid Architecture.- Unsupervised Learning.- Supervised Learning.- II. Applications.- Recognition of Meter Values.- Binarization of Matrix Codes.- Learning Iterative Image Reconstruction.- Face Localization.- Summary and Conclusions.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreSpringer
- Data di pubblicazione2008
- ISBN 10 3540407227
- ISBN 13 9783540407225
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero di pagine240
- Contatto del produttorenon disponibile
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Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science, 2766)
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Condizione: 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. Codice articolo Z1-L-025-01301
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Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science)
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Springer 00/n /13 J, 2003
ISBN 10: 3540407227
ISBN 13: 9783540407225
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Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science)
Editore:
Springer 00/n /13 J, 2003
ISBN 10: 3540407227
ISBN 13: 9783540407225
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Hierarchical Neural Networks for Image Interpretation
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ISBN 10: 3540407227
ISBN 13: 9783540407225
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Softcover. Condizione: 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. Codice articolo 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. Codice articolo 71746
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Hierarchical Neural Networks for Image Interpretation
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Springer Berlin Heidelberg, 2003
ISBN 10: 3540407227
ISBN 13: 9783540407225
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Kartoniert / Broschiert. Condizione: 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. Codice articolo 4888915
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Hierarchical Neural Networks for Image Interpretation
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Taschenbuch. Condizione: 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. Codice articolo 9783540407225
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Hierarchical Neural Networks for Image Interpretation
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Springer Berlin Heidelberg, 2003
ISBN 10: 3540407227
ISBN 13: 9783540407225
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Taschenbuch. Condizione: 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. Codice articolo 9783540407225
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Hierarchical Neural Networks for Image Interpretation
ISBN 10: 3540407227
ISBN 13: 9783540407225
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783540407225
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Hierarchical Neural Networks for Image Interpretation
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