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Proceedings Paper

Research on Bayes matting algorithm based on Gaussian mixture model
Author(s): Wei Quan; Shan Jiang; Cheng Han; Chao Zhang; Zhengang Jiang
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Paper Abstract

The digital matting problem is a classical problem of imaging. It aims at separating non-rectangular foreground objects from a background image, and compositing with a new background image. Accurate matting determines the quality of the compositing image. A Bayesian matting Algorithm Based on Gaussian Mixture Model is proposed to solve this matting problem. Firstly, the traditional Bayesian framework is improved by introducing Gaussian mixture model. Then, a weighting factor is added in order to suppress the noises of the compositing images. Finally, the effect is further improved by regulating the user's input. This algorithm is applied to matting jobs of classical images. The results are compared to the traditional Bayesian method. It is shown that our algorithm has better performance in detail such as hair. Our algorithm eliminates the noise well. And it is very effectively in dealing with the kind of work, such as interested objects with intricate boundaries.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981310 (14 December 2015); doi: 10.1117/12.2208991
Show Author Affiliations
Wei Quan, Changchun Univ. of Science and Technology (China)
Shan Jiang, Changchun Univ. of Science and Technology (China)
Cheng Han, Changchun Univ. of Science and Technology (China)
Chao Zhang, Changchun Univ. of Science and Technology (China)
Zhengang Jiang, Changchun Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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