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Graph-Based Representations in Pattern Recognition

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

  • Conference proceedings
  • © 2005
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Overview

Editors:
  1. Luc Brun

    1. GREYC CNRS UMR 6072, Image Team, Université de Caen Basse-Normandie, Caen Cedex, France

  2. Mario Vento

    1. Dipartimento di Ingegneria dell’Informazione ed Ingegneria Elettrica, Università di Salerno, Fisciano, Italy

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3434)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

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Conference proceedings info: GbRPR 2005.

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About this book

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.

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Table of contents (36 papers)

  1. Front Matter

    Download chapter PDF

  2. Graph Representations

    1. Hypergraph-Based Image Representation

      • Alain Bretto, Luc Gillibert
      Pages 1-11

    2. Vectorized Image Segmentation via Trixel Agglomeration

      • Lakshman Prasad, Alexei N. Skourikhine
      Pages 12-22

    3. Graph Transformation in Document Image Analysis: Approaches and Challenges

      • Dorothea Blostein
      Pages 23-34

    4. Graphical Knowledge Management in Graphics Recognition Systems

      • Mathieu Delalandre, Eric Trupin, Jacques Labiche, Jean-Marc Ogier
      Pages 35-44

    5. A Vascular Network Growth Estimation Algorithm Using Random Graphs

      • Sung-Hyuk Cha, Michael L. Gargano, Louis V. Quintas, Eric M. Wahl
      Pages 45-53

  3. Graphs and Linear Representations

    1. A Linear Generative Model for Graph Structure

      • Bin Luo, Richard C. Wilson, Edwin R. Hancock
      Pages 54-62

    2. Graph Seriation Using Semi-definite Programming

      • Hang Yu, Edwin R. Hancock
      Pages 63-71

    3. Comparing String Representations and Distances in a Natural Images Classification Task

      • Julien Ros, Christophe Laurent, Jean-Michel Jolion, Isabelle Simand
      Pages 72-81

    4. Reduction Strings: A Representation of Symbolic Hierarchical Graphs Suitable for Learning

      • Mickaël Melki, Jean-Michel Jolion
      Pages 82-91

  4. Combinatorial Maps

    1. Representing and Segmenting 2D Images by Means of Planar Maps with Discrete Embeddings: From Model to Applications

      • Achille Braquelaire
      Pages 92-121

    2. Inside and Outside Within Combinatorial Pyramids

      • Luc Brun, Walter Kropatsch
      Pages 122-131

    3. The GeoMap: A Unified Representation for Topology and Geometry

      • Hans Meine, Ullrich Köthe
      Pages 132-141

    4. Pyramids of n-Dimensional Generalized Maps

      • Carine Grasset-Simon, Guillaume Damiand, Pascal Lienhardt
      Pages 142-152

  5. Matching

    1. Towards Unitary Representations for Graph Matching

      • David Emms, Simone Severini, Richard C. Wilson, Edwin R. Hancock
      Pages 153-161

    2. A Direct Algorithm to Find a Largest Common Connected Induced Subgraph of Two Graphs

      • Bertrand Cuissart, Jean-Jacques Hébrard
      Pages 162-171

    3. Reactive Tabu Search for Measuring Graph Similarity

      • Sébastien Sorlin, Christine Solnon
      Pages 172-182

    4. Tree Matching Applied to Vascular System

      • Arnaud Charnoz, Vincent Agnus, Grégoire Malandain, Luc Soler, Mohamed Tajine
      Pages 183-192

  6. Hierarchical Graph Abstraction and Matching

    1. A Graph-Based, Multi-resolution Algorithm for Tracking Objects in Presence of Occlusions

      • Donatello Conte, Pasquale Foggia, Jean-Michel Jolion, Mario Vento
      Pages 193-202

    2. Coarse-to-Fine Object Recognition Using Shock Graphs

      • Aurelie Bataille, Sven Dickinson
      Pages 203-212
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  1. Graph-Based Representations in Pattern Recognition

Editors and Affiliations

  • GREYC CNRS UMR 6072, Image Team, Université de Caen Basse-Normandie, Caen Cedex, France

    Luc Brun

  • Dipartimento di Ingegneria dell’Informazione ed Ingegneria Elettrica, Università di Salerno, Fisciano, Italy

    Mario Vento

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