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Applied Graph Theory in Computer Vision and Pattern Recognition
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2007.
|
| Edición: | 1st ed. 2007. |
| Colección: | Studies in Computational Intelligence,
52 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-540-68020-8 | ||
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| 024 | 7 | |a 10.1007/978-3-540-68020-8 |2 doi | |
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| 245 | 1 | 0 | |a Applied Graph Theory in Computer Vision and Pattern Recognition |h [electronic resource] / |c edited by Abraham Kandel, Horst Bunke, Mark Last. |
| 250 | |a 1st ed. 2007. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2007. | |
| 300 | |a X, 266 p. |b online resource. | ||
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| 490 | 1 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 52 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Engineering mathematics. | |
| 650 | 0 | |a Engineering-Data processing. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Mathematical and Computational Engineering Applications. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 700 | 1 | |a Kandel, Abraham. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Bunke, Horst. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Last, Mark. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 830 | 0 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 52 | |
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