Graph-Based Representations in Pattern Recognition

7th IAPR-TC-15 International Workshop, GbRPR 2009, Venice, Italy, May 26-28, 2009. Proceedings

AvAndrea Torsello,Francisco Escolano Ruiz

Häftad, Engelska, 2009

Del i serien Lecture Notes in Computer Science

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Beskrivning

This volume contains the papers presented at the 7th IAPR-TC-15 Workshop onGraph-BasedRepresentationsinPatternRecognition- GbR2009.Thewo- shop was held in Venice, Italy between May 26-28, 2009. The previous wo- shops in the series were held in Lyon, France (1997), Haindorf, Austria (1999), Ischia, Italy (2001), York, UK (2003), Poitiers, France (2005), and Alicante, Spain (2007). The Technical Committee (TC15, http://www.greyc.ensicaen.fr/iapr-tc15/) of the IAPR (International Association for Pattern Recognition) was founded in order to federate and to encourage research work at the intersection of pattern recognition and graph theory. Among its activities, the TC15 encourages the organization of special graph sessions in many computer vision conferences and organizes the biennial GbR Workshop. The scienti?c focus of these workshops coversresearchin pattern recognition and image analysis within the graph theory framework. This workshop series traditionally provide a forum for presenting and discussing research results and applications in the intersection of pattern recognition, image analysis and graph theory.

Produktinformation

Utgivningsdatum:2009-05-12
Mått:155 x 235 x 22 mm
Vikt:593 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:378
Upplaga:2009
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783642021237

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Innehållsförteckning

Graph-Based Representation and Recognition.- Matching Hierarchies of Deformable Shapes.- Edition within a Graph Kernel Framework for Shape Recognition.- Coarse-to-Fine Matching of Shapes Using Disconnected Skeletons by Learning Class-Specific Boundary Deformations.- An Optimisation-Based Approach to Mesh Smoothing: Reformulation and Extensions.- Graph-Based Representation of Symbolic Musical Data.- Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods.- Computing and Visualizing a Graph-Based Decomposition for Non-manifold Shapes.- A Graph Based Data Model for Graphics Interpretation.- Tracking Objects beyond Rigid Motion.- Graph-Based Registration of Partial Images of City Maps Using Geometric Hashing.- Graph Matching.- A Polynomial Algorithm for Submap Isomorphism.- A Recursive Embedding Approach to Median Graph Computation.- Efficient Suboptimal Graph Isomorphism.- Homeomorphic Alignment of Edge-Weighted Trees.- Inexact Matching of Large and Sparse Graphs Using Laplacian Eigenvectors.- Graph Matching Based on Node Signatures.- A Structural and Semantic Probabilistic Model for Matching and Representing a Set of Graphs.- Arc-Consistency Checking with Bilevel Constraints: An Optimization.- Graph Clustering and Classification.- Pairwise Similarity Propagation Based Graph Clustering for Scalable Object Indexing and Retrieval.- A Learning Algorithm for the Optimum-Path Forest Classifier.- Improving Graph Classification by Isomap.- On Computing Canonical Subsets of Graph-Based Behavioral Representations.- Object Detection by Keygraph Classification.- Graph Regularisation Using Gaussian Curvature.- Characteristic Polynomial Analysis on Matrix Representations of Graphs.- Flow Complexity: Fast Polytopal Graph Complexity and 3D Object Clustering.- Pyramids, Combinatorial Maps, and Homologies.- Irregular Graph Pyramids and Representative Cocycles of Cohomology Generators.- Annotated Contraction Kernels for Interactive Image Segmentation.- 3D Topological Map Extraction from Oriented Boundary Graph.- An Irregular Pyramid for Multi-scale Analysis of Objects and Their Parts.- A First Step toward Combinatorial Pyramids in n-D Spaces.- Cell AT-Models for Digital Volumes.- From Random to Hierarchical Data through an Irregular Pyramidal Structure.- Graph-Based Segmentation.- Electric Field Theory Motivated Graph Construction for Optimal Medical Image Segmentation.- Texture Segmentation by Contractive Decomposition and Planar Grouping.- Image Segmentation Using Graph Representations and Local Appearance and Shape Models.- Comparison of Perceptual Grouping Criteria within an Integrated Hierarchical Framework.