Michael S. Ryoo, D.Eng.

Affiliations: 
2008 Electrical and Computer Engineering University of Texas at Austin, Austin, Texas, U.S.A. 
Area:
Electronics and Electrical Engineering, Computer Science
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"Michael Ryoo"

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J K. Aggarwal grad student 2008 UT Austin
 (Semantic representation and recognition of human activities.)
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Publications

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Wu A, Piergiovanni AJ, Ryoo MS. (2020) Model-Based Robot Imitation with Future Image Similarity International Journal of Computer Vision. 128: 1360-1374
Ryoo MS, Matthies L. (2016) Video-based convolutional neural networks for activity recognition from robot-centric videos Proceedings of Spie. 9837
Ryoo MS, Fuchs TJ, Xia L, et al. (2015) Robot-Centric Activity Prediction from First-Person Videos: What Will They Do to Me? Acm/Ieee International Conference On Human-Robot Interaction. 2015: 295-302
Xia L, Gori I, Aggarwal JK, et al. (2015) Robot-centric activity recognition from first-person RGB-D videos Proceedings - 2015 Ieee Winter Conference On Applications of Computer Vision, Wacv 2015. 357-364
Ryoo MS, Rothrock B, Matthies L. (2015) Pooled motion features for first-person videos Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 7: 896-904
Ryoo MS, Matthies L. (2015) First-Person Activity Recognition: Feature, Temporal Structure, and Prediction International Journal of Computer Vision
Iwashita Y, Takamine A, Kurazume R, et al. (2014) First-person animal activity recognition from egocentric videos Proceedings - International Conference On Pattern Recognition. 4310-4315
Mann S, Kitani KM, Lee YJ, et al. (2014) An introduction to the 3rd workshop on egocentric (first-person) vision Ieee Computer Society Conference On Computer Vision and Pattern Recognition Workshops. 827-832
Ryoo MS, Matthies L. (2013) First-person activity recognition: What are they doing to me? Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2730-2737
Aggarwal JK, Ryoo MS. (2012) Toward a unified framework of motion understanding Image and Vision Computing. 30: 465-466
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