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Learning similarity metrics for dynamic scene segmentation
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
This paper addresses the segmentation of videos with arbitrary motion, including dynamic textures, using novel motion features and a supervised learning approach. Dynamic textures are commonplace in natural scenes, and exhibit complex patterns of appearance and motion (e.g. water, smoke, swaying foliage). These are difficult for existing segmentation algorithms, often violate the brightness constancy assumption needed for optical flow, and have complex segment characteristics beyond uniform
doi:10.1109/cvpr.2015.7298820
dblp:conf/cvpr/TeneyBKH15
fatcat:5lgmacmdvjfs5l7vzuapcae5lm