no code implementations • 30 Mar 2024 • Patrick Guidotti
A kernel based method is proposed for the construction of signature (defining) functions of subsets of $\mathbb{R}^d$.
no code implementations • 29 Aug 2022 • Patrick Guidotti
We revisit the classical kernel method of approximation/interpolation theory in a very specific context motivated by the desire to obtain a robust procedure to approximate discrete data sets by (super)level sets of functions that are merely continuous at the data set arguments but are otherwise smooth.
no code implementations • 13 Mar 2017 • Mariane B. Neiva, Patrick Guidotti, Odemir M. Bruno
The main purpose of this paper is to propose a new preprocessing step in order to improve local feature descriptors and texture classification.