000 | 03968nam a22005055i 4500 | ||
---|---|---|---|
001 | 978-3-540-68020-8 | ||
003 | DE-He213 | ||
005 | 20161121231201.0 | ||
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 |
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 | _aApplied mathematics. | |
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 |
_aKandel, Abraham. _eeditor. |
|
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 | ||
950 | _aEngineering (Springer-11647) | ||
999 |
_c509781 _d509781 |