Graph Based Representations in Pattern Recognition
Published on 22. July 1998
Book
Paperback/Softback
VII, 145 pages
978-3-211-83121-2 (ISBN)
Description
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.
More details
Series
Edition
Softcover reprint of the original 1st ed. 1998
Language
English
Place of publication
Vienna
Austria
Publishing group
Springer Wien
Target group
Professional and scholarly
Research
Illustrations
55 s/w Abbildungen
VII, 145 p. 55 illus.
Dimensions
Height: 244 mm
Width: 170 mm
Thickness: 9 mm
Weight
289 gr
ISBN-13
978-3-211-83121-2 (9783211831212)
DOI
10.1007/978-3-7091-6487-7
Schweitzer Classification
Other editions
Additional editions
Jean-Michel Jolion | Walter Kropatsch
Graph Based Representations in Pattern Recognition
E-Book
12/2012
Springer
€53.49
Available for download
Content
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.