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...
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| Hlavní autor: | |
|---|---|
| Další autoři: | , , |
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2007. |
| Edice: | Studies in Computational Intelligence
52 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Applied Graph Theory in Computer Vision and Pattern Recognition, Texte imprimé, 9783540680192 • Applied Graph Theory in Computer Vision and Pattern Recognition, Texte imprimé, 9783642087646 • Applied Graph Theory in Computer Vision and Pattern Recognition, Texte imprimé, 9783540833697 • Applied Graph Theory in Computer Vision and Pattern Recognition, Texte imprimé, 9783540680192 |
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| 082 | |a 519 | ||
| 100 | 1 | |a Kandel, Abraham, |d 1941- | |
| 245 | 1 | 0 | |a Applied Graph Theory in Computer Vision and Pattern Recognition |c edited by Abraham Kandel, Horst Bunke, Mark Last. |
| 250 | |a 1st ed. 2007. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 52 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 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 | ||
| 700 | 1 | |a Bunke, Horst. |4 edt | |
| 700 | 1 | |a Last, Mark. |4 edt | |
| 700 | 1 | |a Bunke, Horst, |d 1949- |4 pbd | |
| 700 | 1 | |a Last, Mark. |4 pbd | |
| 776 | 0 | |t Applied Graph Theory in Computer Vision and Pattern Recognition |b Texte imprimé |z 9783540680192 | |
| 776 | 0 | |t Applied Graph Theory in Computer Vision and Pattern Recognition |b Texte imprimé |z 9783642087646 | |
| 776 | 0 | |t Applied Graph Theory in Computer Vision and Pattern Recognition |b Texte imprimé |z 9783540833697 | |
| 776 | 0 | |t Applied Graph Theory in Computer Vision and Pattern Recognition |b Texte imprimé |z 9783540680192 | |
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