How to adaptively choose optimal neighborhoods is very important to pixel-domain image denoising algorithms since too many neighborhoods may cause over-smooth artifacts and too fe...
In this paper, we propose a robust method to estimate the fundamental matrix in the presence of outliers. The new method uses random minimum subsets as a search engine to find inli...
Methods based on 1-relaxation, such as basis pursuit and the Lasso, are very popular for sparse regression in high dimensions. The conditions for success of these methods are now ...
Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values), or a prior probability model for...
Abstract— In previous work, we studied the structure and performance of optimum maximum-likelihood receivers for binary antipodal and orthogonal signals in the presence of Gaussi...