Applied Graph Theory in Computer Vision and Pattern Recognition Book

Part I: Applied Graph Theory for Low Level Image Processing and Segmentation.- Multi-resolution Image Segmentations in Graph Pyramids.- A Graphical Model Framework for Image Segmentation.- Digital Topologies on Graphs.- Part II: 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.- Part III: Special Applications.- Graph-Based and Structural Methods for Fingerprint Classification.- Graph Sequence Visualization and Its Application to Computer Network Monitoring and Abnormal Event Detection.- Clustering of Web Documents Using Graph Matching.