Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present a method for the temporal tracking of fluid flow velocity fields. The technique  propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Ito diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements extracted from the image sequence. In order to handle a state space of reasonable dimension, the motion field is represented as a combination of adapted basis functions, derived from a discretization of the vorticity map of the fluid flow velocity field. The resulting nonlinear filtering problem is solved with the particle filter algorithm in continuous time. An adaptive dimensional reduction method is applied to the filtering technique, relying on dynamical systems theory. The efficiency of the tracking method is demonstrated on synthetic and real-world sequences.
['Anne Cuzol', 'Etienne Mémin']
I am writing a research paper on a idea A Stochastic Filtering Technique for Fluid Flow Velocity Fields Tracking
[{"from": "human", "value": "I am writing a research paper on a idea A Stochastic Filtering Technique for Fluid Flow Velocity Fields Tracking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present a method for the temporal tracking of fluid flow velocity fields. The technique  propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Ito diffusion process coming from a stochastic formulation of the vorticity-velocity form of the Navier-Stokes equation and discrete measurements extracted from the image sequence. In order to handle a state space of reasonable dimension, the motion field is represented as a combination of adapted basis functions, derived from a discretization of the vorticity map of the fluid flow velocity field. The resulting nonlinear filtering problem is solved with the particle filter algorithm in continuous time. An adaptive dimensional reduction method is applied to the filtering technique, relying on dynamical systems theory. The efficiency of the tracking method is demonstrated on synthetic and real-world sequences."}]