Performance of optical flow techniques
International Journal of Computer Vision
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Background Layer Model for Object Tracking Through Occlusion
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Video-rate stereo depth measurement on programmable hardware
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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We introduce a generative model of dense flow fields within a layered representation of 3-dimensional scenes. Using probabilistic inference and learning techniques (namely, variational methods), we solve the inverse problem and locally segment the foreground from the background, estimate the nonuniform motion of each, and fill in the disocclusions. To illustrate the usefulness of both the representation and the estimation algorithm, we show results on stabilization and frame interpolation that are obtained by generating from the trained models.