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1.
J Acoust Soc Am ; 146(4): 2596, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31671978

RESUMO

The estimation of poroelastic material parameters based on ultrasound measurements is considered. The acoustical characterisation of poroelastic materials based on various measurements is typically carried out by minimising a cost functional of model residuals, such as the least squares functional. With a limited number of unknown parameters, least squares type approaches can provide both reliable parameter and error estimates. With an increasing number of parameters, both the least squares parameter estimates and, in particular, the error estimates often become unreliable. In this paper, the estimation of the material parameters of an inhomogeneous poroelastic (Biot) plate in the Bayesian framework for inverse problems is considered. Reflection and transmission measurements are performed and 11 poroelastic parameters, as well as 4 measurement setup-related nuisance parameters, are estimated. A Markov chain Monte Carlo algorithm is employed for the computational inference to assess the actual uncertainty of the estimated parameters. The results suggest that the proposed approach for poroelastic material characterisation can reveal the heterogeneities in the object, and yield reliable parameter and uncertainty estimates.

2.
IEEE Trans Pattern Anal Mach Intell ; 31(1): 158-63, 2009 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-19029553

RESUMO

We address the problem of human motion tracking by registering a surface to 3-D data. We propose a method that iteratively computes two things: Maximum likelihood estimates for both the kinematic and free-motion parameters of an articulated object, as well as probabilities that the data are assigned either to an object part, or to an outlier cluster. We introduce a new metric between observed points and normals on one side, and a parameterized surface on the other side, the latter being defined as a blending over a set of ellipsoids. We claim that this metric is well suited when one deals with either visual-hull or visual-shape observations. We illustrate the method by tracking human motions using sparse visual-shape data (3-D surface points and normals) gathered from imperfect silhouettes.


Assuntos
Algoritmos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Articulações/fisiologia , Modelos Biológicos , Movimento/fisiologia , Reconhecimento Automatizado de Padrão/métodos , Inteligência Artificial , Simulação por Computador , Humanos , Articulações/anatomia & histologia
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