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
Full text not available from this repository.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: |
05 Dec 2022 14:18 |
Publisher DOI: |
10.1007/978-3-540-68020-8 |
URI: |
https://boris.unibe.ch/id/eprint/26464 (FactScience: 72048) |
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