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Automatic Foreground Extraction of Head Shoulder Images

  • Jin Wang
  • Yiting Ying
  • Yanwen Guo
  • Qunsheng Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)

Abstract

Most existing techniques of foreground extracting work only in interactive mode. This paper introduces a novel algorithm of automatic foreground extraction for special object, and verifies its effectiveness with head shoulder images. The main contribution of our idea is to make the most use of the prior knowledge to constrain the processing of foreground extraction. For human head shoulder images, we first detect face and a few facial features, which helps to estimate an approximate mask covering the interesting region. The algorithm then extracts the hard edge of foreground from the specified area using an iterative graph cut method incorporated with an improved Gaussian Mixture Model. To generate accurate soft edges, a Bayes matting is applied. The whole process is fully automatic. Experimental results demonstrate that our algorithm is both robust and efficient.

Keywords

Gaussian Mixture Model Face Detection Hard Edge Soft Edge Face Detection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jin Wang
    • 1
  • Yiting Ying
    • 2
  • Yanwen Guo
    • 2
    • 3
  • Qunsheng Peng
    • 2
  1. 1.Xuzhou Normal UniversityXuzhouChina
  2. 2.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina
  3. 3.School of Computer Science and TechnologyShandong UniversityJinanChina

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