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ROAM: A Rich Object Appearance Model with Application to Rotoscoping.

, , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 42 (8): 1996-2010 (2020)

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Motion Textures: Modeling, Classification, and Segmentation Using Mixed-State Markov Random Fields., , , and . SIAM J. Imaging Sci., 6 (4): 2484-2520 (2013)Model Distribution Dependant Complexity Estimation on Textures., , and . ISVC (3), volume 6455 of Lecture Notes in Computer Science, page 271-279. Springer, (2010)Discovering motion hierarchies via tree-structured coding of trajectories., , , and . BMVC, BMVA Press, (2016)Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation., , , and . ICIP, page 1857-1860. IEEE, (2006)Temporal modeling of motion textures with mixed-sates Markov chains., , , and . ICASSP, page 881-884. IEEE, (2008)ROAM: A Rich Object Appearance Model with Application to Rotoscoping., , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 42 (8): 1996-2010 (2020)Object-guided motion estimation., , and . Comput. Vis. Image Underst., (2016)Learning how to be robust: Deep polynomial regression., , , and . CoRR, (2018)Recognition of Dynamic Video Contents Based on Motion Texture Statistical Models., , , and . VISAPP (1), page 283-289. INSTICC - Institute for Systems and Technologies of Information, Control and Communication, (2008)Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random Field., , , and . Int. J. Comput. Vis., 94 (3): 295-316 (2011)