Applied Graph Theory in Computer Vision and Pattern Recognition

• Corporate Author: SpringerLink (Online service)
• Other Authors: Kandel, Abraham (Editor), Bunke, Horst (Editor), Last, Mark (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
• Series: Studies in Computational Intelligence, 52
• Subjects: Engineering.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

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...

Table of Contents:

• Applied Graph Theory for Low Level Image Processing and Segmentation
• Multiresolution Image Segmentations in Graph Pyramids
• A Graphical Model Framework for Image Segmentation
• Digital Topologies on Graphs
• Graph Similarity, Matching, and Learning for High Level Computer Vision and Pattern Recognition
• How and Why Pattern Recognition and Computer Vision Applications Use Graphs
• Efficient Algorithms on Trees and Graphs with Unique Node Labels
• A Generic Graph Distance Measure Based on Multivalent Matchings
• Learning from Supervised Graphs
• Special Applications
• Graph-Based and Structural Methods for Fingerprint Classification
• Graph Sequence Visualisation and its Application to Computer Network Monitoring and Abnormal Event Detection
• Clustering of Web Documents Using Graph Representations.