[ABE-L] title and abstract of my talk

[Alexandre Galvão Patriota]

Prezados,

A apresentação será no IME-USP. Desculpem-me o lapso.

Abraços

On Wed, Nov 21, 2018, 12:24 PM Alexandre Galvão Patriota <
patriota.alexandre em gmail.com wrote:

> Prezados,
>
> Na próxima sexta-feira (23/11) às 14hr, haverá um seminário na sala A132
> do Dr.  Ali Al-Sharadqah, professor na Califorina State University at
> Northridge, cujos titulo e resumo se encontram abaixo.
>
> *Title*:  Statistical Optimization in Geometric Estimation
>
> * Abstract*:  We will introduce Errors-in-Variables models (EIV) and its
> applications in geometric estimation, a widely known problem in computer
> vision and image processing. Two problems from geometric estimation will be
> discussed: (1) fitting geometric curves, such as circles/or ellipses to a
> set of noisy experimental observations, (2) other applications in computer
> vision, such as, `Fundamental Matrix' estimation and `Homography'
> computation that are essential in 3D-reconstruction.
>
> Under our adopted statistical assumptions, the `Maximum Likelihood
> Estimator' (MLE) coincides with the so-called `Orthogonal Distance
> Regression' (ODR), which can be obtained iteratively by the Levenberg
> Marquardt algorithm. Therefore, other non-iterative but less accurate
> algebraic estimators were proposed. In spite of the superior performance of
> the MLE and the adequate performance of some algebraic methods, they all
> have an infinite first moment, while the least accurate algebraic estimator
> has a finite first moment. These controversial results led to some
> methodological questions that require further investigation. Therefore, we
> developed our unconventional statistical analysis that allowed us to
> effectively assess EIV parameter estimates in a general scheme. Then we
> theoretically compared between the most popular fits for circle fitting
> problem and we showed why and by how much each fit differs from others. The
> problem of ellipse fitting is more involved, and as such, we present other
> gradient-type iterative methods and we discuss the statistical properties
> of ellipse-fitting methods. Our theoretical comparisons led to new
> unbeatable fits with superior characteristics that surpass all existing
> methods theoretically and experimentally. At the end of the talk, we will
> discuss how these methods can be extended to the aforementioned computer
> vision applications.
>
> Saudações
> Alexandre.
>
>
>
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