Segmenting shape using deformation information

Ruiqi Guo, Shinichiro Omachi, Hirotomo Aso

Research output: Contribution to journalArticlepeer-review

Abstract

To segment a shape into parts is an important problem in shape representation and analysis. We propose in this paper a novel framework of shape segmentation using deformation models learned from multiple shapes. The deformation model from the target image to every other image is then estimated. Finally, normalized-cut graph partition is applied to the graph constructed based on the similarity of local patches in the target image, and a segmentation of the shape is carried out. Experimental results for images from MPEG7 shape database show the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)1296-1303
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE92-D
Issue number6
DOIs
Publication statusPublished - 2009

Keywords

  • Deformation model
  • Graph partition
  • Shape matching
  • Shape segmentation

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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