We propose an approximate Bayesian approach for unsupervised feature selection and density estimation, where the importance of the features for clustering is used as the measure f...
This paper presents a novel distributed framework for multi-target tracking with an efficient data association computation. A decentralized representation of trackers' motion...
In this paper we propose a method for matching articulated shapes represented as large sets of 3D points by aligning the corresponding embedded clouds generated by locally linear ...
Diana Mateus, Fabio Cuzzolin, Radu Horaud, Edmond ...
Existing sequential feature-based registration algorithms involving search typically either select features randomly (eg. the RANSAC[8] approach) or assume a predefined, intuitive...
We propose a convex framework for silhouette and stereo fusion in 3D reconstruction from multiple images. The key idea is to show that the reconstruction problem can be cast as one...