The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

Underwater computer vision has attracted increasing attention in the research community
due to the recent advances in underwater platforms such as of rovers, gliders, autonomous …

Deep neural networks have provided state-of-the-art solutions for problems such as image
denoising, which implicitly rely on a prior probability model of natural images. Two recent …

One of the defining properties of deep learning is that models are chosen to have many
more parameters than available training data. In light of this capacity for overfitting, it is …

We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc,
generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By …

Semidefinite Programming (SDP) and Sums-of-Squares (SOS) relaxations have led to
certifiably optimal non-minimal solvers for several robotics and computer vision problems …

A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …

Sparse approximate solutions to linear equations are classically obtained via L1 norm
regularized least squares, but this method often underestimates the true solution. As an …

We show in this paper that the success of previous maximum a posterior (MAP) based blur
removal methods partly stems from their respective intermediate steps, which implicitly or …

Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …