Looking around in our every day environment, many of the encountered objects are specular to some degree. Actively using this fact when reconstructing objects from image sequences...
This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gau...
Nicolas Dobigeon, Alfred O. Hero, Jean-Yves Tourne...
In this paper, we present a new Adaptive Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANSAC (R...
We consider the problem of fitting one or more subspaces to a collection of data points drawn from the subspaces and corrupted by noise/outliers. We pose this problem as a rank m...
Sparse Orthonormal Transforms (SOT) has recently been proposed as a data compression method that can achieve sparser representations in transform domain. Given initial conditions,...