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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Kandel, Abraham (Editor ), Bunke, Horst (Editor ), Last, Mark (Editor )
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:
Engineering mathematics.
Engineering-Data processing.
Artificial intelligence.
Mathematical and Computational Engineering Applications.
Artificial Intelligence.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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