TY  - BOOK
AU  - Kandel,Abraham
AU  - Bunke,Horst
AU  - Last,Mark
ED  - SpringerLink (Online service)
TI  - Applied Graph Theory in Computer Vision and Pattern Recognition
T2  - Studies in Computational Intelligence,
SN  - 9783540680208
AV  - TA329-348 
U1  - 519 23
PY  - 2007///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Engineering mathematics
KW  - Appl.Mathematics/Computational Methods of Engineering
KW  - Artificial Intelligence (incl. Robotics)
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-540-68020-8
ER  -