A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
IEEE Computer Graphics and Applications
ACM SIGGRAPH 2003 Papers
Local Color Transfer via Probabilistic Segmentation by Expectation-Maximization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Automated colour grading using colour distribution transfer
Computer Vision and Image Understanding
Non-rigid dense correspondence with applications for image enhancement
ACM SIGGRAPH 2011 papers
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In a stereo camera set-up for 3D cinema, much care is taken to ensure that the cameras are both the same model and use identical parameter values. But an imperfect adjustment, the use of beam splitters, as well as unavoidable differences in the characteristics of the cameras and lens systems, often cause visible differences in color among the two views. Many of these differences appear to be local and therefore cannot be fully removed by common color matching approaches, which are typically global methods. We propose a simple yet effective method for local color matching, that operates in three steps. First, one view is chosen as the target and morphed so that it becomes locally registered with the source view, the one whose colors will be modified. Next, color matching is performed on the source view by aligning each local histogram with the corresponding local histogram in the warped target view. Finally, Poisson editing [9] is applied on the source regions which have no correspondence in the target. For several professional, high quality examples of 3D sequences, we show that our algorithm outperforms four different conventional color matching approaches.