About this book
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.
- DOI https://doi.org/10.1007/b11963
- Copyright Information Springer-Verlag Berlin Heidelberg 2003
- Publisher Name Springer, Berlin, Heidelberg
- eBook Packages Springer Book Archive
- Print ISBN 978-3-540-40722-5
- Online ISBN 978-3-540-45169-3
- Series Print ISSN 0302-9743
- Series Online ISSN 1611-3349
- About this book