A graph representation for silhouette based on multiscale analysis

Ming Ming Zhang, Shinichiro Omachi, Hirotomo Aso

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Graph descriptor is usually focused on in computer vision for its flexibility and richness. However, in object recognition, it is difficult to catch the feature of an object completely with a straightforward way by a graph. In this paper, from a multiscale viewpoint, we propose a method to construct a vertex-labeled graph for image recognition where the label represents the importance of a vertex to the graph. By using the Fourier Descriptor, when we adjust the cut-off frequency we can select how much detailed feature can be used to represent a contour. In this process, we found the medial axis of shape also evolves from a simple graph to a detailed graph. Focusing on this evolution, for each vertex in the graph we assign a label representing its importance in this graph.

Original languageEnglish
Title of host publicationProceedings of the 2009 Chinese Conference on Pattern Recognition, CCPR 2009, and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR
Pages837-839
Number of pages3
DOIs
Publication statusPublished - 2009
Event2009 Chinese Conference on Pattern Recognition, CCPR 2009 and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR - Nanjing, China
Duration: 2009 Nov 42009 Nov 6

Publication series

NameProceedings of the 2009 Chinese Conference on Pattern Recognition, CCPR 2009, and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR

Other

Other2009 Chinese Conference on Pattern Recognition, CCPR 2009 and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR
Country/TerritoryChina
CityNanjing
Period09/11/409/11/6

Keywords

  • Fourier descriptor
  • Graph recognition
  • Graph representation
  • Medial axis
  • Multiscale analysis
  • Silhouette image

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Software

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