Abstract. Establishing correspondence between features of a set of images has been a long-standing issue amongst the computer vision community. We propose a method that solves the ...
In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in computer vision. Each of these techniques constructs...
We present a subspace learning method, called Local Discriminant Embedding with Tensor representation (LDET), that addresses simultaneously the generalization and data representat...
In this paper we introduce a block-based approach for ordinal co-occurrence matrices aimed at improving robustness of the basic ordinal co-occurrence. Earlier, we have introduced t...
This paper introduces a sparse signal representation algorithm in redundant dictionaries, called the M-Term Pursuit (MTP), with an application to image representation and scalable ...
Adel Rahmoune, Pierre Vandergheynst, Pascal Frossa...