From Gestalt Theory to Image Analysis: A Probabilistic Approach (Interdisciplinary Applied Mathematics, 34)

Desolneux, Agnès; Moisan, Lionel; Morel, Jean-Michel

ISBN 10: 0387726357 / ISBN 13: 9780387726359

Published by Springer, 2007

About this Item

Seller Inventory # SHUB87661

Bibliographic Details

• Title: From Gestalt Theory to Image Analysis: A Probabilistic Approach (Interdisciplinary Applied Mathematics, 34)
• Publisher: Springer
• Publication Date: 2007
• Language: English
• ISBN 10: 0387726357
• ISBN 13: 9780387726359
• Binding: Hardcover
• Condition: New

About this title

Synopsis

This richly illustrated book introduces the reader to a newly developed theory in Computer Vision yielding elementary techniques to analyze digital images. These techniques are inspired by, and are a mathematical formalization of the Gestalt theory. The authors maintain a publicly available software program called MegaWave, containing implementations of most of the image analysis techniques developed in the book. The volume is intended for a multidisciplinary audience of researchers and engineers. It is mathematically self-contained and requires only the basic understanding of probability and calculus. The text contains more than 130 illustrations. The authors offer numerous examples based on specific images on which the theory is tested. Detailed exercises at the end of each chapter help the reader develop a firm understanding of the concepts imparted.

From the Back Cover

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.