Abstract--Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context. In this paper we show that the detection perfor...
Diego Tosato, Michela Farenzena, Marco Cristani, V...
Abstract. Finding appropriate parameter values for Evolutionary Algorithms (EAs) is one of the persistent challenges of Evolutionary Computing. In recent publications we showed how...
The paper has two main contributions: The rst is a set of methods for computing structure and motion for m 3 views of 6 points. It is shown that a geometric image error can be mini...
Frederik Schaffalitzky, Andrew Zisserman, Richard ...
We describe and analyze a new approach for feature ranking in the presence of categorical features with a large number of possible values. It is shown that popular ranking criteria...
Abstract— Accurate self-localization capability is highly desirable in wireless sensor networks. A major problem in wireless sensor network localization is the flip ambiguity, w...