Learning an intrinsic-Variable preserving manifold for dynamic visual tracking

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 40, Issue 3, 2010, Pages 868-880

  Qiao, Hong ^a   Zhang, Peng ^b   Zhang, Bo ^b   Zheng, Suiwu ^a  

^a INSTITUTE OF AUTOMATION   (China)

^b INSTITUTE OF APPLIED MATHEMATICS   (China)

Author keywords

Feature extraction; Robotic visual tracking; Visual tracking

Indexed keywords

DIMENSIONALITY REDUCTION; DYNAMIC TRACKING; DYNAMIC VISION; FREE-MOVING OBJECTS; HIGH-DIMENSIONAL; LEARNING APPROACH; LEARNING FRAMEWORKS; LOW DIMENSIONAL; LOW-DIMENSIONAL MANIFOLDS; MANIFOLD LEARNING; NONLINEAR DIMENSIONALITY REDUCTION; REAL TIME TRACKING; THEORETICAL POINTS; TRAINING SAMPLE; VISUAL TRACKING;

FEATURE EXTRACTION; ROBOTICS; ROBOTS;

TRACKING (POSITION);

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 77952581490     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2009.2031559     Document Type: Article

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