Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2766)
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Table of contents (11 chapters)
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Front Matter
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Introduction
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Part I. Theory
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Front Matter
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Part II. Applications
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Front Matter
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Back Matter
Keywords
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.
Reviews
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)
Authors and Affiliations
Bibliographic Information
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
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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