Graph-Based Representations in Pattern Recognition : 5th Iapr International Workshop, Gbrpr 2005, Poitiers, France, April 11-13, 2005, Proceedings - Luc Brun

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Graph-Based Representations in Pattern Recognition

5th Iapr International Workshop, Gbrpr 2005, Poitiers, France, April 11-13, 2005, Proceedings

By: Luc Brun (Editor), Mario Vento (Editor)

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Paperback | 23 March 2005

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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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You Can Find This Book In

Non-Fiction Mathematics Combinatorics & Graph Theory Computing & I.T.Computer Science Artificial Intelligence Computer Vision Pattern Recognition Graphical & Digital Media Applications 3D Graphics & Modelling Databases Data Capture & Analysis

Hypergraph-based image representation	p. 1
Vectorized image segmentation via trixel agglomeration	p. 12
Graph transformation in document image analysis : approaches and challenges	p. 23
Graphical knowledge management in graphics recognition systems	p. 35
A vascular network growth estimation algorithm using random graphs	p. 45
A linear generative model for graph structure	p. 54
Graph seriation using semi-definite programming	p. 63
Comparing string representations and distances in a natural images classification task	p. 72
Reduction strings : a representation of symbolic hierarchical graphs suitable for learning	p. 82
Representing and segmenting 2D images by means of planar maps with discrete embeddings : from model to applications	p. 92
Inside and outside within combinatorial pyramids	p. 122
The GeoMap : a unified representation for topology and geometry	p. 132
Pyramids of n-Dimensional generalized maps	p. 142
Towards unitary representations for graph matching	p. 153
A direct algorithm to find a largest common connected induced subgraph of two graphs	p. 162
Reactive tabu search for measuring graph similarity	p. 172
Tree matching applied to vascular system	p. 183
A graph-based, multi-resolution algorithm for tracking objects in presence of occlusions	p. 193
Coarse-to-fine object recognition using shock graphs	p. 203
Adaptive pyramid and semantic graph : knowledge driven segmentation	p. 213
A graph-based concept for spatiotemporal information in cognitive vision	p. 223
Approximating the problem, not the solution : an alternative view of point set matching	p. 233
Defining consistency to detect change using inexact graph matching	p. 243
Asymmetric inexact matching of spatially-attributed graphs	p. 253
From exact to approximate maximum common subgraph	p. 263
Automatic learning of structural models of cartographic objects	p. 273
An experimental comparison of fingerprint classification methods using graphs	p. 281
Collaboration between statistical and structural approaches for old handwritten characters recognition	p. 291
Decision trees for error-tolerant graph database filtering	p. 301
Recovery of missing information in graph sequences	p. 312
Tree-based tracking of temporal image	p. 322
Protein classification with kernelized softassign	p. 332
Local entropic graphs for globally-consistent graph matching	p. 342
Edit distance based kernel functions for attributed graph matching	p. 352
A robust graph partition method from the path-weighted adjacency matrix	p. 362
Recent results on heat kernel embedding of graphs	p. 373
Table of Contents provided by Blackwell. All Rights Reserved.

Graph-Based Representations in Pattern Recognition

5th Iapr International Workshop, Gbrpr 2005, Poitiers, France, April 11-13, 2005, Proceedings

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