Context and observation driven latent variable model for human pose estimation

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Gupta, Abhinav ^a   Chen, Trista ^b   Chen, Francine ^b   Kimber, Don ^b   Davis, Larry S ^a  

^a UNIVERSITY OF MARYLAND   (United States)

^b FX PALO ALTO LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; ESTIMATION; FEATURE EXTRACTION; GESTURE RECOGNITION; IMAGE PROCESSING; MOTION ESTIMATION; PATTERN RECOGNITION;

COMPUTATIONALLY EFFICIENT; COMPUTATIONALLY EXPENSIVE; GAUSSIAN PROCESSES; GENERATIVE MODELING; HIGH-DIMENSIONAL; HUMAN POSE; HYBRID MODEL; IMAGE FEATURES; INTEGRATED LEARNING; JOINT ANGLES; LATENT VARIABLE MODELING; PARAMETERIZED; POSE-ESTIMATION; SYSTEMATIC VARIATIONS;

MODAL ANALYSIS;

EID: 51949097702     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587511     Document Type: Conference Paper

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