We propose a novel probabilistic framework for learning
visual models of 3D object categories by combining appearance
information and geometric constraints. Objects are
represen...
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
This paper describes experiments in automatic recognition of context-independent phoneme strings from meeting data using audiovisual features. Visual features are known to improve ...
In this paper a gesture recognition system using 3D data is described. The system relies on a novel 3D sensor that generates a dense range image of the scene. The main novelty of ...
Sotiris Malassiotis, Niki Aifanti, Michael G. Stri...
Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...