Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant...
Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the ...
In this paper we present a novel learning based method for restoring and recognizing images of digits that have been blurred using an unknown kernel. The novelty of our work is an...
Mithun Das Gupta, ShyamSundar Rajaram, Nemanja Pet...
We investigate to what extent `bag of visual words' models can be used to distinguish categories which have significant visual similarity. To this end we develop and optimize...
We propose a manifold learning approach to fiber tract clustering using a novel similarity measure between fiber tracts constructed from dual-rooted graphs. In particular, to gene...
Andy Tsai, Carl-Fredrik Westin, Alfred O. Hero, Al...