Hierarchical Neural Networks for Image Interpretation
Language: English
Published by Springer, 2003
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 57.87
Quantity: 1 available
Add to basketItem description from seller
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
- Title
- Hierarchical Neural Networks for Image Interpretation
- Author
- Sven Behnke
- Publisher
- Springer
- Publication year
- 2003
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3540407227
- ISBN 13
- 9783540407225
- Item weight
- 371 grams
- Dimensions
- 235x155x14 mm
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 the title" may belong to another edition of this title.
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