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A Bayesian approach to digital matting
Abstract
This paper proposes a new Bayesian framework for solving the matting problem, i.e. extracting a foreground element from a background image by estimating an opacity for each pixel of the foreground element. Our approach models both the foreground and background color distributions with spatially-varying sets of Gaussians, and assumes a fractional blending of the foreground and background colors to produce the final output. It then uses a maximum-likelihood criterion to estimate the optimal opacity, foreground and background simultaneously. In addition to providing a principled approach to the matting problem, our algorithm effectively handles objects with intricate boundaries, such as hair strands and fur, and provides an improvement over existing techniques for these difficult cases.
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Citations
Cited by
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
A Closed-Form Solution to Natural Image Matting
Levin, A., Lischinski, D., & Weiss, Y. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1177 · Cited by 1,616
Interactive Image Segmentation Using an Adaptive GMMRF Model
Blake, A., Rother, C., Brown, M., Perez, P., & Torr, P. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24670-1_33 · Cited by 527
A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting
Bai, X., & Sapiro, G. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408931 · Cited by 438
An iterative optimization approach for unified image segmentation and matting
Wang, J., & Cohen, M. F. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.37 · Cited by 316
Metadata
- Authors
- Chuang, Y.-Y., Curless, B., Salesin, D. H., & Szeliski, R.
- Venue
- Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
- Publisher
- IEEE Comput. Soc
- Published
- 2005-08-24
- Volume
- 2
- Pages
- II-264-II-271
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2103917701
- Cited by
- 796
- References
- 0
- FWCI
- 46.11
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chuang, Y.-Y., Curless, B., Salesin, D. H., & Szeliski, R. (2005). A Bayesian approach to digital matting. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. https://doi.org/10.1109/cvpr.2001.990970
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