Graph Based Representations in Pattern Recognition

Graph-based representation of images is becoming a popular tool since it represents in a compact way the structure of a scene to be analyzed and allows for an easy manipulation of sub-parts or of relationships between parts. Therefore, it is widely used to control the different levels from segmentation to interpretation. The 14 papers in this volume are grouped in the following subject areas: hypergraphs, recognition and detection, matching, segmentation, implementation problems, representation.

Contenu
Hypergraphs.- Generalization of Two Hypergraphs. Algorithm of Calculation of the Greatest Sub-Hypergraph Common to Two Hypergraphs Annotated by Semantic Information.- Recognition and Detection.- Recognition of Polymorphic Patterns in Parameterized Graphs for 3D Building Reconstruction.- A Graph-Based Representation to Detect Linear Features.- Edge Detection as Finding the Minimum Cost Path in a Graph.- Matching.- Subgraph Transformations for the Inexact Matching of Attributed Relational Graphs.- Efficient Graph Matching for Video Indexing.- Isomorphism between Strong Fuzzy Relational Graphs Based on k-Formulae.- Segmentation.- A Graph Structure for Grey Value and Texture Segmentation.- Discrete Maps: a Framework for Region Segmentation Algorithms.- Image Sequence Segmentation by a Single Evolutionary Graph Pyramid.- Implementation Problems.- Dual Graph Contraction with LEDA.- Implementing Image Analysis with a Graph-Based Parallel Computing Model.- Representation.- The Frontier-Region Graph.- Optimization Techniques on Pixel Neighborhood Graphs for Image Processing.