Recognizing gestures by learning local motion signatures of hog descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 34, Issue 11, 2012, Pages 2247-2258

  Kaâniche, Mohamed Bécha ^a   Brémond, François ^b  

^a UNIVERSITY OF CARTHAGE   (Tunisia)

^b INRIA   (France)

Author keywords

feature tracking; Gesture recognition; HOG descriptors; motion detection; probabilistic learning and classification

Indexed keywords

CODE-WORDS; CODEBOOKS; DESCRIPTORS; FEATURE-TRACKING; HISTOGRAMS OF ORIENTED GRADIENTS; K-MEANS ALGORITHM; K-NEAREST NEIGHBORS (K-NN) ALGORITHMS; LOCAL FEATURE; LOCAL MOTIONS; MOTION DETECTION; MUTUAL INFORMATIONS; PROBABILISTIC LEARNING; STATE-OF-THE-ART METHODS; VIDEO DATABASE; VOTING STRATEGIES;

GESTURE RECOGNITION;

CLUSTERING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; GESTURE; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MOVEMENT (PHYSIOLOGY); PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VIDEORECORDING; WHOLE BODY IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; GESTURES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MOVEMENT; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; VIDEO RECORDING; WHOLE BODY IMAGING;

EID: 84866693713     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2012.19     Document Type: Article

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