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Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions
Computer Vision, IEEE International Conference on
By Peter Ochs,Thomas Brox
Issue Date:November 2011
Point trajectories have emerged as a powerful means to obtain high quality and fully unsupervised segmentation of objects in video shots. They can exploit the long term motion difference between objects, but they tend to be sparse due to computational reas...
Segmentation of Moving Objects by Long Term Video Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
By Peter Ochs,Jitendra Malik,Thomas Brox
Issue Date:December 2013
Motion is a strong cue for unsupervised object-level grouping. In this paper, we demonstrate that motion will be exploited most effectively, if it is regarded over larger time windows. Opposed to classical two-frame optical flow, point trajectories that sp...
An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision
2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Peter Ochs,Alexey Dosovitskiy,Thomas Brox,Thomas Pock
Issue Date:June 2013
Natural image statistics indicate that we should use non-convex norms for most regularization tasks in image processing and computer vision. Still, they are rarely used in practice due to the challenge to optimize them. Recently, iteratively reweighed 1 mi...
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