Applied Graph Theory in Computer Vision Pattern Recognition
This book presents novel graph-theoretic methods for complex computer vision and pattern recognition tasks. It presents the application of graph theory to low-level processing of digital images, presents graph-theoretic learning algorithms for high-level...
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This book presents novel graph-theoretic methods for complex computer vision and pattern recognition tasks. It presents the application of graph theory to low-level processing of digital images, presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, and provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks.
- Multi-resolution Image Segmentations in Graph Pyramids
- A Graphical Model Framework for Image Segmentation
- Digital Topologies on Graphs
Part II: 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
Part III: Special Applications
- Graph-Based and Structural Methods for Fingerprint Classification
- Graph Sequence Visualization and Its Application to Computer Network Monitoring and Abnormal Event Detection
- Clustering of Web Documents Using Graph Matching
- 2007, X, 265 Seiten, 85 Schwarz-Weiß-Abbildungen, Maße: 16,1 x 24,4 cm, Gebunden, Englisch
- Herausgegeben: Abraham Kandel, Horst Bunke, Mark Last
- Verlag: Springer Berlin
- ISBN-10: 3540680195
- ISBN-13: 9783540680192
- Erscheinungsdatum: 12.03.2007
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