Lecture Notes in Computer Science

Behnke, Sven

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.

Details

Published by: Springer

Publication Date: 2003-08-21

Format: Paperback

ISBN-13: 9783540407225

DOI: 10.1007/b11963

Dimensions: 235cm x155cm

Pages: 227