E-Book Overview
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.
E-Book Content
Sven Behnke
Hierarchical Neural Networks for Image Interpretation
June 13, 2003
Draft submitted to Springer-Verlag Published as volume 2766 of Lecture Notes in Computer Science ISBN: 3-540-40722-7
Foreword
It is my pleasure and privilege to write the foreword for this book, whose results I have been following and awaiting for the last few years. This monograph represents the outcome of an ambitious project oriented towards advancing our knowledge of the way the human visual system processes images, and about the way it combines high level hypotheses with low level inputs during pattern recognition. The model proposed by Sven Behnke, carefully exposed in the following pages, can be applied now by other researchers to practical problems in the field of computer vision and provides also clues for reaching a deeper understanding of the human visual system. This book arose out of dissatisfaction with an earlier project: back in 1996, Sven wrote one of the handwritten digit recognizers for the mail sorting machines of the Deutsche Post AG. The pro