30 citations · 30 across the 11 of their papers we have counts for
13 papers
A three-step framework for noisy image segmentation in brain MRI
Laura Antonelli, Valentina De Simone, Marco Viola
Magnetic Resonance Imaging (MRI) is essential for noninvasive generation of high-quality images of human tissues. Accurate segmentation of MRI data is critical for medical applicat…
A Stochastic Birth-and-Death Approach for Street Furniture Geolocation in Urban Environments
Evan Murphy, Marco Viola, Vladimir A. Krylov
In this paper we address the problem of precise geolocation of street furniture in complex urban environments, which is a critical task for effective monitoring and maintenance of…
Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
Gabriele Sanguin, Arjun Pakrashi, Marco Viola +1
Handling uncertainty is critical for ensuring reliable decision-making in intelligent systems. Modern neural networks are known to be poorly calibrated, resulting in predicted conf…
An algorithm for a constrained P-spline
Rosanna Campagna, Serena Crisci, Gabriele Santin +2
Regression splines are largely used to investigate and predict data behavior, attracting the interest of mathematicians for their beautiful numerical properties, and of statisticia…
Cartoon-texture evolution for two-region image segmentation
Laura Antonelli, Valentina De Simone, Marco Viola
Two-region image segmentation is the process of dividing an image into two regions of interest, i.e., the foreground and the background. To this aim, Chan et al. [Chan, Esedoglu, N…
Directional TGV-based image restoration under Poisson noise
Daniela di Serafino, Germana Landi, Marco Viola
We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for exa…
TGV-based restoration of Poissonian images with automatic estimation of the regularization parameter
Daniela di Serafino, Germana Landi, Marco Viola
The problem of restoring images corrupted by Poisson noise is common in many application fields and, because of its intrinsic ill posedness, it requires regularization techniques f…
Sparse Approximations with Interior Point Methods
Valentina De Simone, Daniela di Serafino, Jacek Gondzio +2
Large-scale optimization problems that seek sparse solutions have become ubiquitous. They are routinely solved with various specialized first-order methods. Although such methods a…
LSOS: Line-search Second-Order Stochastic optimization methods for nonconvex finite sums
Daniela di Serafino, Nataša Krejić, Nataša Krklec Jerinkić +1
We develop a line-search second-order algorithmic framework for minimizing finite sums. We do not make any convexity assumptions, but require the terms of the sum to be continuousl…
Using gradient directions to get global convergence of Newton-type methods
Daniela di Serafino, Gerardo Toraldo, Marco Viola
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [IMA Journal of Numerical Analysis, 8 (1988)] has driven us to consider a globaliza…
A subspace-accelerated split Bregman method for sparse data recovery with joint l1-type regularizers
Valentina De Simone, Daniela di Serafino, Marco Viola
We propose a subspace-accelerated Bregman method for the linearly constrained minimization of functions of the form ,…
ACQUIRE: an inexact iteratively reweighted norm approach for TV-based Poisson image restoration
Daniela di Serafino, Germana Landi, Marco Viola
We propose a method, called ACQUIRE, for the solution of constrained optimization problems modeling the restoration of images corrupted by Poisson noise. The objective function is…
A two-phase gradient method for quadratic programming problems with a single linear constraint and bounds on the variables
Daniela di Serafino, Gerardo Toraldo, Marco Viola +1
We propose a gradient-based method for quadratic programming problems with a single linear constraint and bounds on the variables. Inspired by the GPCG algorithm for bound-constrai…