Applied Graph Theory in Computer Vision and Pattern Recognition

  • Abraham Kandel
  • Horst Bunke
  • Mark Last

Part of the Studies in Computational Intelligence book series (SCI, volume 52)

Table of contents

  1. Front Matter
    Pages I-X
  2. Applied Graph Theory for Low Level Image Processing and Segmentation

    1. Front Matter
      Pages 1-1
    2. Walter G. Kropatsch, Yll Haxhimusa, Adrian Ion
      Pages 3-41
    3. Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
      Pages 43-63
    4. Alain Bretto
      Pages 65-82
  3. Graph Similarity, Matching, and Learning for High Level Computer Vision and Pattern Recognition

    1. Front Matter
      Pages 84-84
    2. Donatello Conte, Pasquale Foggia, Carlo Sansone, Mario Vento
      Pages 85-135
    3. Sébastien Sorlin, Christine Solnon, Jean-Michel Jolion
      Pages 151-181
    4. Joseph Potts, Diane J. Cook, Lawrence B. Holder
      Pages 183-201
  4. Special Applications

    1. Front Matter
      Pages 204-204
    2. Gian Luca Marcialis, Fabio Roli, Alessandra Serrau
      Pages 205-226
    3. Horst Bunke, P. Dickinson, A. Humm, Ch. Irniger, M. Kraetzl
      Pages 227-245
    4. Adam Schenker, Horst Bunke, Mark Last, Abraham Kandel
      Pages 247-265

About this book

Introduction

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.

Keywords

Matching algorithm algorithms classification cognition computer vision fingerprint graph theory graphs image processing image segmentation learning network pattern pattern recognition

Editors and affiliations

  • Abraham Kandel
    • 1
  • Horst Bunke
    • 2
  • Mark Last
    • 3
  1. 1.Computer Science & Engineering DepartmentUniversity of South FloridaTampaUSA
  2. 2.Institute of Computer Science and Applied Mathematics (IAM)BernSwitzerland
  3. 3.Department of Information Systems EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-68020-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-68019-2
  • Online ISBN 978-3-540-68020-8
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book