Applied Graph Theory in Computer Vision and Pattern Recognition: Studies in Computational Intelligence, cartea 52
Editat de Abraham Kandel, Horst Bunke, Mark Lasten Limba Engleză Hardback – 12 mar 2007
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Specificații
ISBN-13: 9783540680192
ISBN-10: 3540680195
Pagini: 276
Ilustrații: X, 266 p.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.56 kg
Ediția:2007
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540680195
Pagini: 276
Ilustrații: X, 266 p.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.56 kg
Ediția:2007
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
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.
Textul de pe ultima copertă
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.