Graph-Based Representations in Pattern Recognition

5th IAPR International Workshop, GbRPR 2005, Poitiers, France, April 11-13, 2005. Proceedings

  • Luc Brun
  • Mario Vento
Conference proceedings GbRPR 2005
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3434)

Table of contents

  1. Front Matter
  2. Graph Representations

    1. Alain Bretto, Luc Gillibert
      Pages 1-11
    2. Lakshman Prasad, Alexei N. Skourikhine
      Pages 12-22
    3. Mathieu Delalandre, Eric Trupin, Jacques Labiche, Jean-Marc Ogier
      Pages 35-44
    4. Sung-Hyuk Cha, Michael L. Gargano, Louis V. Quintas, Eric M. Wahl
      Pages 45-53
  3. Graphs and Linear Representations

    1. Bin Luo, Richard C. Wilson, Edwin R. Hancock
      Pages 54-62
    2. Hang Yu, Edwin R. Hancock
      Pages 63-71
    3. Julien Ros, Christophe Laurent, Jean-Michel Jolion, Isabelle Simand
      Pages 72-81
  4. Combinatorial Maps

    1. Luc Brun, Walter Kropatsch
      Pages 122-131
    2. Hans Meine, Ullrich Köthe
      Pages 132-141
    3. Carine Grasset-Simon, Guillaume Damiand, Pascal Lienhardt
      Pages 142-152
  5. Matching

    1. David Emms, Simone Severini, Richard C. Wilson, Edwin R. Hancock
      Pages 153-161
    2. Bertrand Cuissart, Jean-Jacques Hébrard
      Pages 162-171
    3. Sébastien Sorlin, Christine Solnon
      Pages 172-182
    4. Arnaud Charnoz, Vincent Agnus, Grégoire Malandain, Luc Soler, Mohamed Tajine
      Pages 183-192
  6. Hierarchical Graph Abstraction and Matching

    1. Donatello Conte, Pasquale Foggia, Jean-Michel Jolion, Mario Vento
      Pages 193-202
    2. Aurelie Bataille, Sven Dickinson
      Pages 203-212

About these proceedings

Introduction

Many vision problems have to deal with di?erent entities (regions, lines, line junctions, etc.) and their relationships. These entities together with their re- tionships may be encoded using graphs or hypergraphs. The structural inf- mation encoded by graphs allows computer vision algorithms to address both the features of the di?erent entities and the structural or topological relati- ships between them. Moreover, turning a computer vision problem into a graph problem allows one to access the full arsenal of graph algorithms developed in computer science. The Technical Committee (TC15, http://www.iapr.org/tcs.html) of the IAPR (International Association for Pattern Recognition) has been funded in order to federate and to encourage research work in these ?elds. Among its - tivities, TC15 encourages the organization of special graph sessions at many computer vision conferences and organizes the biennial workshop GbR. While being designed within a speci?c framework, the graph algorithms developed for computer vision and pattern recognition tasks often share constraints and goals with those developed in other research ?elds such as data mining, robotics and discrete geometry. The TC15 community is thus not closed in its research ?elds but on the contrary is open to interchanges with other groups/communities.

Keywords

classification computational graph theory computer vision database filtering fingerprint graph matching graph-based methods graph-based representations graph-theoretic methods image analysis object recognition pattern recognition programming topology

Editors and affiliations

  • Luc Brun
    • 1
  • Mario Vento
    • 2
  1. 1.GREYC CNRS UMR 6072, Image TeamUniversité de Caen Basse-NormandieCaen CedexFrance
  2. 2.Dipartimento di Ingegneria dell’Informazione ed Ingegneria ElettricaUniversità di SalernoFiscianoItaly

Bibliographic information

  • DOI https://doi.org/10.1007/b107037
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-25270-2
  • Online ISBN 978-3-540-31988-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349