A stochastic filtering technique for fluid flow velocity fields tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 31, Issue 7, 2009, Pages 1278-1293

  Cuzol, Anne ^a   Mémin, Etienne ^b  

^a UNIVERSITÉ EUROPÉENNE DE BRETAGNE   (France)

^b INRIA   (France)

Author keywords

Fluid flows; Motion estimation; Nonlinear stochastic filtering; Tracking

Indexed keywords

ADAPTED BASIS; BAYESIAN FILTERING FRAMEWORKS; CONTINUOUS TIME; DIFFUSION PROCESS; DIMENSIONAL REDUCTION; DISCRETIZATION; DYNAMICAL SYSTEMS THEORY; FILTERING MODELS; FILTERING TECHNIQUE; FLUID FLOW VELOCITY; FLUID FLOWS; IMAGE SEQUENCE; MOTION FIELDS; NON-LINEAR FILTERING PROBLEMS; NONLINEAR STOCHASTIC FILTERING; PARTICLE FILTER ALGORITHMS; REAL-WORLD SEQUENCES; STATE SPACE; STOCHASTIC FILTERING; STOCHASTIC FORMULATION; TRACKING; TRACKING METHOD;

DYNAMICAL SYSTEMS; FLOW VELOCITY; FLUIDS; MOTION ESTIMATION; NAVIER STOKES EQUATIONS; NONLINEAR FILTERING; RANDOM PROCESSES; TARGET TRACKING; VELOCITY; VORTICITY;

STOCHASTIC MODELS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; FLOW KINETICS; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; MOTION; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MODELS, STATISTICAL; MOTION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; RHEOLOGY; SENSITIVITY AND SPECIFICITY; STOCHASTIC PROCESSES; SUBTRACTION TECHNIQUE;

EID: 67349220255     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2008.152     Document Type: Article

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