Feature extraction and soft segmentation of texture images

  • Fang Li*
  • , Ruihua Liu
  • , Chaomin Shen
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

(Corresponding author) In this paper, we propose an efficient method for texture image segmentation. First we extract four feature channels smoothed with the total variation (TV) flow. Then we propose a soft segmentation model based on the ChanVese model by adding a weight in the arc length term and using a soft membership function in stead of level set function to represent the region. We derive a fast algorithm using the Additive Operator Scheme (AOS) and Chambolle's fast dual projection method. Experimental results on texture and Synthetic Aperture Radar (SAR) images show the effectiveness of our algorithm.

Original languageEnglish
Title of host publicationIASP 10 - 2010 International Conference on Image Analysis and Signal Processing
Pages367-370
Number of pages4
DOIs
StatePublished - 2010
Event2nd International Conference on Image Analysis and Signal Processing, IASP'2010 - Xiamen, China
Duration: 12 Apr 201014 Apr 2010

Publication series

NameIASP 10 - 2010 International Conference on Image Analysis and Signal Processing

Conference

Conference2nd International Conference on Image Analysis and Signal Processing, IASP'2010
Country/TerritoryChina
CityXiamen
Period12/04/1014/04/10

Keywords

  • Chan-Vese model
  • Dual projection method
  • Feature extraction
  • Soft segmentation
  • Texture

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