SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ACM SIGGRAPH 2004 Papers
An Iterative Optimization Approach for Unified Image Segmentation and Matting
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and video matting: a survey
Foundations and Trends® in Computer Graphics and Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A global sampling method for alpha matting
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Current image matting methods based on color sampling use color to distinguish between foreground and background pixels. However, they fail when the corresponding color distributions overlap. Other methods that define correlation between neighboring pixels based on color aim to propagate the opacity parameter @a from known pixels to unknown pixels. However, strong edges of textured regions may block the propagation of @a. In this paper, a new matting strategy is proposed that delivers an accurate matte by considering texture as a feature that can complement color even if the foreground and background color distributions overlap and the image is a complex one with highly textured regions. The texture feature is extracted in such a way as to increase distinction between foreground and background regions. An objective function containing color and texture components is optimized to find the best foreground and background pair among a set of candidate pairs. The effectiveness of proposed method is compared quantitatively as well as qualitatively with other matting methods by evaluating their results on a benchmark dataset and a set of complex images. The evaluations show that the proposed method presented the best among state of the art matting methods.