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14 December 2015 Research on Bayes matting algorithm based on Gaussian mixture model
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Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 981310 (2015)
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wei Quan, Shan Jiang, Cheng Han, Chao Zhang, and Zhengang Jiang "Research on Bayes matting algorithm based on Gaussian mixture model", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981310 (14 December 2015);


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