000 03929nam a22004935i 4500
001 978-3-540-68020-8
003 DE-He213
005 20170628034616.0
007 cr nn 008mamaa
008 100301s2007 gw | s |||| 0|eng d
020 _a9783540680208
_9978-3-540-68020-8
024 7 _a10.1007/978-3-540-68020-8
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aKandel, Abraham.
_eeditor.
245 1 0 _aApplied Graph Theory in Computer Vision and Pattern Recognition
_h[electronic resource] /
_cedited by Abraham Kandel, Horst Bunke, Mark Last.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2007.
300 _aX, 266 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v52
505 0 _aApplied 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.
520 _aThis 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aBunke, Horst.
_eeditor.
700 1 _aLast, Mark.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540680192
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v52
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-68020-8
912 _aZDB-2-ENG
999 _c20658
_d20658