Hierarchical Neural Networks for Image Interpretation

  • Sven┬áBehnke

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2766)

Table of contents

  1. Front Matter
  2. Introduction

    1. Sven Behnke
      Pages 1-13
  3. Part I. Theory

    1. Front Matter
      Pages 15-15
    2. Sven Behnke
      Pages 17-33
    3. Sven Behnke
      Pages 35-63
    4. Sven Behnke
      Pages 65-94
    5. Sven Behnke
      Pages 95-110
    6. Sven Behnke
      Pages 111-126
  4. Part II. Applications

    1. Front Matter
      Pages 127-127
    2. Sven Behnke
      Pages 129-147
    3. Sven Behnke
      Pages 149-165
    4. Sven Behnke
      Pages 167-190
    5. Sven Behnke
      Pages 191-202
    6. Sven Behnke
      Pages 203-207
  5. Back Matter

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.


cognition computer vision control learning neural architectures neural network neural network learning neural networks object recognition pattern recognition perception performance supervised learning unsupervised learning visual perception

Authors and affiliations

  • Sven┬áBehnke
    • 1
  1. 1.Computer Science InstituteUniversity of FreiburgFreiburgGermany

Bibliographic information

  • DOI
  • 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
  • Buy this book on publisher's site