Markov random field modeling in computer vision
Markov random field modeling in computer vision
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH 2004 Papers
An Iterative Bayesian Approach for Digital Matting
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
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This paper proposes a Markov Random Field (MRF) model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions. In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel. All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori (MAP) estimation problem. Simulated annealing is used to find the optimal MAP estimation. The better results can be obtained under the same user-interactions when images are complex. Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.