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AR
2011
12 years 11 months ago
Learning, Generation and Recognition of Motions by Reference-Point-Dependent Probabilistic Models
This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using referenc...
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Sa...
IROS
2008
IEEE
113views Robotics» more  IROS 2008»
13 years 11 months ago
Motion recognition and generation by combining reference-point-dependent probabilistic models
— This paper presents a method to recognize and generate sequential motions for object manipulation such as placing one object on another or rotating it. Motions are learned usin...
Komei Sugiura, Naoto Iwahashi
CVPR
2007
IEEE
14 years 6 months ago
Learning Motion Categories using both Semantic and Structural Information
Current approaches to motion category recognition typically focus on either full spatiotemporal volume analysis (holistic approach) or analysis of the content of spatiotemporal in...
Shu-Fai Wong, Tae-Kyun Kim, Roberto Cipolla
ECCV
2004
Springer
14 years 6 months ago
Extraction of Semantic Dynamic Content from Videos with Probabilistic Motion Models
Abstract. The exploitation of video data requires to extract information at a rather semantic level, and then, methods able to infer "concepts" from low-level video featu...
Gwenaëlle Piriou, Jian-Feng Yao, Patrick Bout...
CVPR
2005
IEEE
14 years 6 months ago
Hybrid Models for Human Motion Recognition
Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the h...
Claudio Fanti, Lihi Zelnik-Manor, Pietro Perona