Applied Graph Theory in Computer Vision and Pattern Recognition...

Features

• Cover Type: Hard Cover with 266 pages
• Published by: Springer
• Edition: 1st Edition April 12, 2007
• Written in: English
• ISBN 10 Number: 3540680195
• ISBN 13 Number: 978-3540680192
• Book Dimensions: 9.3 x 6.6 x 0.8 inches
• Weighs: 1.2 pounds

Product Description


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.