From Gestalt Theory to Image Analysis

A Probabilistic Approach

  • Agnés Desolneux
  • Lionel Moisan
  • Jean-Michel Morel

Part of the Interdisciplinary Applied Mathematics book series (IAM, volume 34)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Pages 1-9
  3. Pages 11-30
  4. Pages 115-131
  5. Pages 133-151
  6. Pages 153-176
  7. Pages 191-202
  8. Pages 203-226
  9. Back Matter
    Pages 261-269

About this book

Introduction

This book introduces the reader to a recent theory in Computer Vision yielding elementary techniques to analyse digital images. These techniques are inspired from and are a mathematical formalization of the Gestalt theory. Gestalt theory, which had never been formalized is a rigorous realm of vision psychology developped between 1923 and 1975.

From the mathematical viewpoint the closest field to it is stochastic geometry, involving basic probability and statistics, in the context of image analysis.

 The book is intended for a multidisciplinary audience of researchers and engineers. It is self contained in three aspects: mathematics, vision and algorithms, and requires only a background of elementary calculus and probability. A large number of illustrations, exercises and examples are included. The authors maintain a public software, MegaWave, containing implementations of most of the image analysis techniques developed in the book.

Keywords

Alignment Analysis Computer Vision Maxima computer image analysis linear optimization statistics

Authors and affiliations

  • Agnés Desolneux
    • 1
  • Lionel Moisan
    • 1
  • Jean-Michel Morel
    • 2
  1. 1.Université Paris Descartes MAP5 (CNRS UMR 8145)Paris cedex 06France
  2. 2.Ecole Normale Supérieure de CachanCMLAMoscowFrance

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-74378-3
  • Copyright Information Springer-Verlag New York 2008
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-72635-9
  • Online ISBN 978-0-387-74378-3
  • Series Print ISSN 0939-6047
  • About this book