Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science) - Tapa blanda

Behnke, Sven

 
9783540407225: Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science)
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ISBN 10: 3540407227 ISBN 13: 9783540407225
Editorial: Springer, 2008

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Críticas

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)

Reseña del editor

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2008
Idioma
Inglés
ISBN 10 
3540407227
ISBN 13 
9783540407225
Encuadernación
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Número de páginas
240
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

Friedrich-Karl-Str. 9
Hamburg
Alemania

Otras ediciones populares con el mismo título

9783662202609: Hierarchical Neural Networks for Image Interpretation

Edición Destacada

ISBN 10:  3662202603 ISBN 13:  9783662202609
Editorial: Springer, 2014
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