In this paper, a novel variational approach is proposed to fit multivariate Gaussians from noisy data in arbitrary dimensions. The proposed FIGARO algorithm is ...

ABSTRACT. Fitting multivariate Gaussian functions constitutes a funda- mental task in many scientific fields. However, most of the.

May 30, 2018 · ABSTRACT. Fitting multivariate Gaussian functions constitutes a funda- mental task in many scientific fields. However, most of the.

Apr 25, 2019 · In this paper, we provide a novel variational formulation of the multivariate Gaussian fitting problem, which is applicable to any dimension.

Abstract Fitting Gaussian functions to empirical data is a crucial task in a variety of scientific applications, especially in image processing.

Fitting Gaussian functions to empirical data is a crucial task in a variety of scientific applications, especially in image processing.

In this paper, a novel variational approach is proposed to fit multivariate Gaus-sians from noisy data in arbitrary dimensions. The proposed FIGARO algorithm is ...

A novel variational formulation of the multivariate Gaussian fitting problem, applicable to any dimension and accounts for possible nonzero background and ...

This computational solution delivers a complete and precise 3D PSF estimation, robust to noise, and anisotropic 3D blur effects which are not negligible with a ...

Multiphoton microscopes provide another axial stacking method and multiparametric Gaussian fittings have proven a valuable approximation to the 3D PSF ( ...