We present a neural system that recognizes faces under strong variations in pose and illumination. The generalization is learnt completely on the basis of examples of a subset of p...
Abstract. In this paper, we propose a novel method for the unsupervised clustering of graphs in the context of the constellation approach to object recognition. Such method is an E...
Boyan Bonev, Francisco Escolano, Miguel Angel Loza...
We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a weakly-supervised manner: the model is learnt from examp...
A likelihood formulation for human tracking is presented based upon matching feature statistics on the surface of an articulated 3D body model. A benefit of such a formulation ove...
Timothy J. Roberts, Stephen J. McKenna, Ian W. Ric...
This paper presents a semi-supervised training method for linear-chain conditional random fields that makes use of labeled features rather than labeled instances. This is accompli...