Overview
- Will serve as a foundation for a variety of useful applications of the graph theory to computer vision, pattern recognition, and related areas
- Covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 52)
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About this book
Graph theory has strong historical roots in mathematics, especially in topology. Its birth is usually associated with the “four-color problem” posed by Francis Guthrie 1 in 1852, but its real origin probably goes back to the Seven Bridges of Konigsber ¨ g 2 problem proved by Leonhard Euler in 1736. A computational solution to these two completely different problems could be found after each problem was abstracted to the level of a graph model while ignoring such irrelevant details as country shapes or cross-river distances. In general, a graph is a nonempty set of points (vertices) and the most basic information preserved by any graph structure refers to adjacency relationships (edges) between some pairs of points. In the simplest graphs, edges do not have to hold any attributes, except their endpoints, but in more sophisticated graph structures, edges can be associated with a direction or assigned a label. Graph vertices can be labeled as well. A graph can be represented graphically as a drawing (vertex=dot,edge=arc),but,aslongaseverypairofadjacentpointsstaysconnected by the same edge, the graph vertices can be moved around on a drawing without changing the underlying graph structure. The expressive power of the graph models placing a special emphasis on c- nectivity between objects has made them the models of choice in chemistry, physics, biology, and other ?elds.
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Table of contents (10 chapters)
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Front Matter
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Applied Graph Theory for Low Level Image Processing and Segmentation
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Front Matter
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Graph Similarity, Matching, and Learning for High Level Computer Vision and Pattern Recognition
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Front Matter
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Editors and Affiliations
Bibliographic Information
Book Title: Applied Graph Theory in Computer Vision and Pattern Recognition
Editors: Abraham Kandel, Horst Bunke, Mark Last
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-540-68020-8
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2007
Hardcover ISBN: 978-3-540-68019-2Published: 12 March 2007
Softcover ISBN: 978-3-642-08764-6Published: 13 November 2010
eBook ISBN: 978-3-540-68020-8Published: 11 April 2007
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 266
Topics: Mathematical and Computational Engineering, Artificial Intelligence