Scale Invariance Based Salient Contour Detection

Jing Wang, Takeshi Ikenaga, Satoshi Goto

Research output: Contribution to journalArticlepeer-review


Contour detection is a fundamental step to scene analysis and interpretation. However, because contours often locate in rich texture background it is still a difficult task in realistic vision. Through multi-scale analysis, it becomes clear that edge responses of real object contours are relatively stable across scales, while those from noise or texture background are not. In this paper, a salient contour detection method is proposed based on the scale invariance of piecewise linear approximation of real object contours. Firstly, an image pyramid is efficiently constructed by repeatedly smoothing and sub-sampling the image. Secondly, the piecewise linear approximation of contours in multiple scales are extracted and then a collinear line grouping process is implemented to improve the connectivity of the contours. Thirdly, the new salient line segments are generated based on the analysis on the stability of line segments across scales. Experimental results show that the proposed method can effectively improve the connectivity and saliency of the contour detection compared with the former method.

Original languageEnglish
Pages (from-to)710-720
Number of pages11
JournalJournal of the Institute of Image Electronics Engineers of Japan
Issue number5
Publication statusPublished - 2007


  • Piecewise Linear Approximation
  • Salient Contour Detection
  • Scale Invariance
  • Texture Background

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering


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