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Perfect Snapping

  • Qingsong Zhu
  • Ling Shao
  • Qi Li
  • Yaoqin Xie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7733)

Abstract

Interactive image matting is a process that extracts a foreground object from an image based on limited user input. In this paper, we propose a novel interactive image matting algorithm named Perfect Snapping which is inspired by the presented method named Lazy Snapping technique. In the algorithm, the mean shift algorithm with a boundary confidence prior is introduced to efficiently pre-segment the original image into homogeneous regions (super-pixels) with precise boundary. Secondly, Gaussian Mixture Model (GMM) clustering algorithm is used to describe and to model the super-pixels. Finally, a Monte Carlo based Expectation Maximization (EM) algorithm is used to perform parametric learning of mixture model for priori knowledge. Experimental results indicate that the proposed algorithm can achieve higher matting quality with higher efficiency.

Keywords

Interactive Image Matting Mean Shift Algorithm Lazy Snapping 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qingsong Zhu
    • 1
    • 2
    • 3
    • 4
  • Ling Shao
    • 5
  • Qi Li
    • 1
    • 6
  • Yaoqin Xie
    • 1
    • 2
    • 3
    • 4
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.Key Lab for Low-Cost HealthcareChinese Academy of SciencesShenzhenChina
  3. 3.Key Lab for Health InformaticsChinese Academy of SciencesShenzhenChina
  4. 4.School of MedicineStanford UniversityStanfordUSA
  5. 5.Department of Electronic and Electrical EngineeringUniversity of SheffieldSheffieldUK
  6. 6.School of Software EngineeringUniversity of Science and Technology of ChinaHefeiChina

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