Video object segmentation with shortest path
Proceedings of the 20th ACM international conference on Multimedia
Multi-scale clustering of frame-to-frame correspondences for motion segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Online moving camera background subtraction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Video segmentation with superpixels
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Online learning for fast segmentation of moving objects
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Adaptive integration of feature matches into variational optical flow methods
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Editor's Choice Article: Motion-based segmentation of objects using overlapping temporal windows
Image and Vision Computing
User-assisted sparse stereo-video segmentation
Proceedings of the 10th European Conference on Visual Media Production
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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 reasons and the difficulty in estimating motion in homogeneous areas. In this paper we introduce a variational method to obtain dense segmentations from such sparse trajectory clusters. Information is propagated with a hierarchical, nonlinear diffusion process that runs in the continuous domain but takes superpixels into account. We show that this process raises the density from 3% to 100% and even increases the average precision of labels.