ENGLISH

Hierarchical Neural Networks for Image Interpretation

By Sven Behnke (auth.)

Book information

Publisher
Springer-Verlag Berlin Heidelberg
Year
2003
ISBN
3540407227, 9783540407225
DOI
10.1007/b11963
ISSN
0302-9743
Open Library ID
OL9309448M
Language
english
Format
PDF
Filesize
8 MB (7975782 bytes)
Series
Lecture Notes in Computer Science 2766
Edition
1
Pages
227\244
Library
Kolxo3
Time added
2009-12-04 00:34:26
Computation by Abstract Devices; Neurosciences; Al

Description

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.

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