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Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation
[article]
2018
arXiv
pre-print
Unsupervised video segmentation plays an important role in a wide variety of applications from object identification to compression. However, to date, fast motion, motion blur and occlusions pose significant challenges. To address these challenges for unsupervised video segmentation, we develop a novel saliency estimation technique as well as a novel neighborhood graph, based on optical flow and edge cues. Our approach leads to significantly better initial foreground-background estimates and
arXiv:1809.01125v1
fatcat:d6vwfn6ypfa5rd2b7gpggks5k4