A geometric contour framework with vector field support

  • Authors:

  • Zhenglong Li;Qingshan Liu;Hanqing Lu

  • Affiliations:

  • National Laboratory of Pattern Recognition, Automation of Institute, Chinese Academy of Sciences, Beijing;National Laboratory of Pattern Recognition, Automation of Institute, Chinese Academy of Sciences, Beijing;National Laboratory of Pattern Recognition, Automation of Institute, Chinese Academy of Sciences, Beijing

  • Venue:
  • ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
  • Year:
  • 2006

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Abstract

In this paper, we propose a new geometric contour framework with support of specified vector field. First we define three criteria for selection of vector field in geometric model. According to the criteria, EdgeFlow, a powerful segmentation tool, is selected to generate desirable initial vector field. In order to overcome the drawbacks of conventional geometric models, multi-source external forces, such as from texture and multi-spectra, are integrated to provide the ability for segmenting the texture-rich and complex scene images. Instead of common smoothing pre-processing to denoise and suppress possible spurious edges, the more advanced complex diffusion filters are adopted in our algorithm, which result in the piecewise filtered image to help detect those sharp transition regions. We test our model on the Berkeley Segmentation Database, and the experimental results are promising.