A bayesian framework for extracting human gait using strong prior knowledge

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 28, Issue 11, 2006, Pages 1738-1752

  Ziheng, Zhou ^b   Prügel Bennett, Adam ^b   Damper, Robert I ^a,b  

^a IEEE   (United Kingdom)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Articulated motion; Bayesian framework; Hidden Markov model; Human gait; Strong prior

Indexed keywords

ARTICULATED MOTION; BAYESIAN FRAMEWORK; HUMAN GAIT; STRONG PRIOR;

BAYESIAN NETWORKS; HIDDEN MARKOV MODELS; LEARNING SYSTEMS; MOTION CONTROL; TRACKING (POSITION); VIDEO SIGNAL PROCESSING;

GAIT ANALYSIS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BIOLOGICAL MODEL; BIOMECHANICS; CLUSTER ANALYSIS; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; GAIT; HUMAN; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; JOINT; METHODOLOGY; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BIOMECHANICS; CLUSTER ANALYSIS; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; GAIT; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; JOINTS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 33947095890     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2006.214     Document Type: Article

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