Latent gaussian mixture regression for human pose estimation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6494 LNCS, Issue PART 3, 2011, Pages 679-690

  Tian, Yan ^a,c   Sigal, Leonid ^b   Badino, Hernán ^c   De La Torre, Fernando ^c   Liu, Yong ^a  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b DISNEY RESEARCH   (United States)

^c CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL DENSITY; CONDITIONAL DISTRIBUTION; DISCRIMINATIVE APPROACH; FUNCTIONAL MAPPING; GAUSSIAN MIXTURES; HIGH DIMENSIONAL SPACES; HUMAN POSE ESTIMATIONS; IMAGE FEATURES; LATENT VARIABLE MODELS; LIMITED TRAINING DATA; LOW-DIMENSIONAL SPACES; MODEL REPRESENTATION; MULTI-MODAL; NON-LINEAR; NON-PARAMETRIC; OVERFITTING; TRAINING SETS;

COMPUTER VISION;

THREE DIMENSIONAL;

EID: 79952492138     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-19318-7_53     Document Type: Conference Paper

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