Translator Disclaimer
14 December 2015 Research on Bayes matting algorithm based on Gaussian mixture model
Author Affiliations +
Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 981310 (2015) https://doi.org/10.1117/12.2208991
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
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.
© (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); https://doi.org/10.1117/12.2208991
PROCEEDINGS
6 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Image encryption using the Sudoku matrix
Proceedings of SPIE (April 28 2010)
Research on texture feature of RS image based on cloud...
Proceedings of SPIE (November 07 2008)
Online image quality surveys based on response time
Proceedings of SPIE (January 28 2008)

Back to Top