RESUMO
In this paper, we present a method for the temporal tracking of fluid flow velocity fields. The technique we propose is formalized within a sequential Bayesian filtering framework. The filtering model combines an Itô 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.
Assuntos
Algoritmos , Inteligência Artificial , Interpretação de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Reconhecimento Automatizado de Padrão/métodos , Reologia/métodos , Técnica de Subtração , Simulação por Computador , Aumento da Imagem/métodos , Modelos Estatísticos , Movimento (Física) , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Processos EstocásticosRESUMO
This paper presents a novel non-rigid registration method. The main contribution of the method is the modeling of the vorticity (respectively divergence) of the deformation field using vortex (respectively sink and source) particles. Two parameters are associated with a particle: the vorticity (or divergence) strength and the influence domain. This leads to a very compact representation of vorticity and divergence fields. In addition, the optimal position of these particles is determined using a mean shift process. 2D experiments of this method are presented and demonstrate its ability to recover evolving phenomena (MS lesions) so as to register images from 20 patients.
