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.

From the reviews:

"This booklet is the reprint of a thesis. It addresses image interpretation using a neural network architecture mimicking the human visual system. … The exposition is divided in two parts, namely theory and applications. … In short this thesis is very interesting, well written and easy to read." (Jean Th. Lapresté, Zentralblatt MATH, Vol. 1041 (16), 2004)

• Computer Science Institute, University of Freiburg, Freiburg, Germany

  Sven Behnke

• Book Title: Hierarchical Neural Networks for Image Interpretation

• Authors: Sven Behnke

• Series Title: Lecture Notes in Computer Science

• DOI: https://doi.org/10.1007/b11963

• Publisher: Springer Berlin, Heidelberg

• eBook Packages: Springer Book Archive

• Copyright Information: Springer-Verlag Berlin Heidelberg 2003

• Softcover ISBN: 978-3-540-40722-5Published: 21 August 2003

• eBook ISBN: 978-3-540-45169-3Published: 18 November 2003

• Series ISSN: 0302-9743

• Series E-ISSN: 1611-3349

• Edition Number: 1

• Number of Pages: XIII, 227

• Topics: Computation by Abstract Devices, Neurosciences, Algorithm Analysis and Problem Complexity, Artificial Intelligence, Image Processing and Computer Vision, Pattern Recognition