In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
In this paper we propose a general framework to solve the articulated shape matching problem, formulated as finding point-to-point correspondences between two shapes represented b...
Diana Mateus, Fabio Cuzzolin, Radu Horaud, Edmond ...
We introduce `Joint Feature Distributions', a general statistical framework for feature based multi-image matching that explicitly models the joint probability distributions ...
We describe a new region descriptor and apply it to two problems, object detection and texture classification. The covariance of d-features, e.g., the three-dimensional color vecto...
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 ...