Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
There have been important recent advances in object recognition through the matching of invariant local image features. However, the existing approaches are based on matching to i...
This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects i...
This article describes a multiple feature data fusion applied to an auxiliary particle filter for markerless tracking of 3D two-arm gestures by using a single camera mounted on a ...
Ordinal regression has become an effective way of learning user preferences, but most of research only focuses on single regression problem. In this paper we introduce collaborati...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...