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Greg Mori

Showing results (1-10 of 15) with videos related to

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IEEE Transactions on Pattern Analysis and Machine Intelligence|January 24, 2017
Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov NetworksHossein Hajimirsadeghi, Greg Mori
IEEE Transactions on Pattern Analysis and Machine Intelligence|June 24, 2006
Recovering 3D human body configurations using shape contextsGreg Mori, Jitendra Malik
IEEE Transactions on Pattern Analysis and Machine Intelligence|August 22, 2009
Human action recognition by semilatent topic modelsYang Wang, Greg Mori
IEEE Transactions on Pattern Analysis and Machine Intelligence|December 8, 2010
Hidden Part Models for Human Action Recognition: Probabilistic versus Max MarginYang Wang, Greg Mori
IEEE Transactions on Pattern Analysis and Machine Intelligence|December 19, 2018
Deep Neural Network Compression by In-Parallel Pruning-QuantizationFrederick Tung, Greg Mori
IEEE Transactions on Pattern Analysis and Machine Intelligence|November 16, 2005
Efficient shape matching using shape contextsGreg Mori, Serge Belongie, Jitendra Malik
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|February 1, 2013
Distinguishing near-falls from daily activities with wearable accelerometers and gyroscopes using Support Vector MachinesOmar Aziz, Edward J Park, Greg Mori, et al.
Gait & Posture|October 24, 2013
Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometersOmar Aziz, Edward J Park, Greg Mori, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence|December 7, 2011
Discriminative latent models for recognizing contextual group activitiesTian Lan, Yang Wang, Weilong Yang, et al.
Medical & Biological Engineering & Computing|April 24, 2016
A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trialsOmar Aziz, Magnus Musngi, Edward J Park, et al.
Pageof 2

Showing results (1-10 of 15) with videos related to

Sort By:
Pageof 2
IEEE Transactions on Pattern Analysis and Machine Intelligence|January 24, 2017
Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov NetworksHossein Hajimirsadeghi, Greg Mori
IEEE Transactions on Pattern Analysis and Machine Intelligence|June 24, 2006
Recovering 3D human body configurations using shape contextsGreg Mori, Jitendra Malik
IEEE Transactions on Pattern Analysis and Machine Intelligence|August 22, 2009
Human action recognition by semilatent topic modelsYang Wang, Greg Mori
IEEE Transactions on Pattern Analysis and Machine Intelligence|December 8, 2010
Hidden Part Models for Human Action Recognition: Probabilistic versus Max MarginYang Wang, Greg Mori
IEEE Transactions on Pattern Analysis and Machine Intelligence|December 19, 2018
Deep Neural Network Compression by In-Parallel Pruning-QuantizationFrederick Tung, Greg Mori
IEEE Transactions on Pattern Analysis and Machine Intelligence|November 16, 2005
Efficient shape matching using shape contextsGreg Mori, Serge Belongie, Jitendra Malik
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|February 1, 2013
Distinguishing near-falls from daily activities with wearable accelerometers and gyroscopes using Support Vector MachinesOmar Aziz, Edward J Park, Greg Mori, et al.
Gait & Posture|October 24, 2013
Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometersOmar Aziz, Edward J Park, Greg Mori, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence|December 7, 2011
Discriminative latent models for recognizing contextual group activitiesTian Lan, Yang Wang, Weilong Yang, et al.
Medical & Biological Engineering & Computing|April 24, 2016
A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trialsOmar Aziz, Magnus Musngi, Edward J Park, et al.
Pageof 2