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Hierarchical Neural Networks for Image Interpretation

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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.

Categories:
Year:
2003
Edition:
1
Publisher:
Springer-Verlag Berlin Heidelberg
Language:
english
Pages:
227 / 244
ISBN 10:
3540407227
ISBN 13:
9783540407225
Series:
Lecture Notes in Computer Science 2766
File:
PDF, 9.22 MB

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