Graph-Based Representations in Pattern Recognition : 5th IAPR International Workshop, GbRPR 2005, Poitiers, France, April 11-13, 2005, Proceedings / edited by Luc Brun, Mario Vento.

Format:

Book

Contributor:

Brun, Luc, editor.

Vento, Mario, 1960- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 3434.

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 3434

Language:

English

Subjects (All):

Pattern perception.

Optical data processing.

Computer graphics.

Computer science--Mathematics.

Computer science.

Data structures (Computer science).

Pattern Recognition.

Image Processing and Computer Vision.

Computer Graphics.

Discrete Mathematics in Computer Science.

Data Structures.

Local Subjects:

Pattern Recognition.

Image Processing and Computer Vision.

Computer Graphics.

Discrete Mathematics in Computer Science.

Data Structures.

Physical Description:

1 online resource (XII, 384 pages).

Edition:

First edition 2005.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.

System Details:

text file PDF

Summary:

Many vision problems have to deal with di?erent entities (regions, lines, line junctions, et cetera) 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.

Contents:

Graph Representations

Hypergraph-Based Image Representation

Vectorized Image Segmentation via Trixel Agglomeration

Graph Transformation in Document Image Analysis: Approaches and Challenges

Graphical Knowledge Management in Graphics Recognition Systems

A Vascular Network Growth Estimation Algorithm Using Random Graphs

Graphs and Linear Representations

A Linear Generative Model for Graph Structure

Graph Seriation Using Semi-definite Programming

Comparing String Representations and Distances in a Natural Images Classification Task

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

Combinatorial Maps

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

Inside and Outside Within Combinatorial Pyramids

The GeoMap: A Unified Representation for Topology and Geometry

Pyramids of n-Dimensional Generalized Maps

Matching

Towards Unitary Representations for Graph Matching

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

Reactive Tabu Search for Measuring Graph Similarity

Tree Matching Applied to Vascular System

Hierarchical Graph Abstraction and Matching

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

Coarse-to-Fine Object Recognition Using Shock Graphs

Adaptive Pyramid and Semantic Graph: Knowledge Driven Segmentation

A Graph-Based Concept for Spatiotemporal Information in Cognitive Vision

Inexact Graph Matching

Approximating the Problem, not the Solution: An Alternative View of Point Set Matching

Defining Consistency to Detect Change Using Inexact Graph Matching

Asymmetric Inexact Matching of Spatially-Attributed Graphs

From Exact to Approximate Maximum Common Subgraph

Learning

Automatic Learning of Structural Models of Cartographic Objects

An Experimental Comparison of Fingerprint Classification Methods Using Graphs

Collaboration Between Statistical and Structural Approaches for Old Handwritten Characters Recognition

Graph Sequences

Decision Trees for Error-Tolerant Graph Database Filtering

Recovery of Missing Information in Graph Sequences

Tree-Based Tracking of Temporal Image

Graph Kernels

Protein Classification with Kernelized Softassign

Local Entropic Graphs for Globally-Consistent Graph Matching

Edit Distance Based Kernel Functions for Attributed Graph Matching

Graphs and Heat Kernels

A Robust Graph Partition Method from the Path-Weighted Adjacency Matrix

Recent Results on Heat Kernel Embedding of Graphs.

Other Format:

Printed edition:

ISBN:

978-3-540-31988-7

9783540319887

Access Restriction:

Restricted for use by site license.