Applied Graph Theory in Computer Vision and Pattern Recognition

Kandel, Abraham; Bunke, Horst; Last, Mark (eds.) (2007). Applied Graph Theory in Computer Vision and Pattern Recognition. Studies in Computational Intelligence: Vol. 52. Heidelberg: Springer Verlag 10.1007/978-3-540-68020-8

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This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Item Type: Book (Edited Volume)
Division/Institute: 08 Faculty of Science > Institute of Computer Science (INF)
UniBE Contributor: Bunke, Horst
ISBN: 978-3-540-68019-2
Series: Studies in Computational Intelligence
Publisher: Springer Verlag
Language: English
Submitter: Factscience Import
Date Deposited: 04 Oct 2013 15:01
Last Modified: 29 Jan 2015 16:11
Publisher DOI: 10.1007/978-3-540-68020-8
URI: (FactScience: 72048)

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