| Dewey Class |
515.353 |
| Title |
From Gestalt Theory to Image Analysis ([Ebook]) : A Probabilistic Approach / by Agnés Desolneux, Lionel Moisan, Jean-Michel Morel. |
| Author |
Desolneux, Agnés |
| Added Personal Name |
Moisan, Lionel |
| Morel, Jean-Michel |
| Other name(s) |
SpringerLink (Online service) |
| Publication |
New York, NY : Springer New York , 2008. |
| Physical Details |
: online resource. |
| Series |
Interdisciplinary Applied Mathematics 0939-6047 ; ; 34 |
| ISBN |
9780387743783 |
| Summary Note |
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.: |
| Contents note |
Introduction -- Gestalt Theory -- The Helmholtz Principle -- Estimating the Binomial Tail -- Alignments in Digital Images -- Maximal Meaningfulness and the Exclusion Principle -- Modes of a Histogram -- Vanishing Points -- Contrasted Boundaries -- Variational or Meaningful Boundaries? -- Clusters -- Binocular Grouping -- A Psychophysical Study of the Helmholtz Principle -- Back to the Gestalt Programme -- Other theories, Discussion. |
| System details note |
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
http://dx.doi.org/10.1007/978-0-387-74378-3 |
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