Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that gr...
Discovering local geometry of low-dimensional manifold embedded into a high-dimensional space has been widely studied in the literature of machine learning. Counter-intuitively, w...
—SIFT-like local feature descriptors are ubiquitously employed in such computer vision applications as content-based retrieval, video analysis, copy detection, object recognition...
Christoph Strecha, Alexander A. Bronstein, Michael...
We propose a method for the analysis of Magnetic Resonance (MR) cardiac images with the goal of reconstructing the motion of the ventricular walls. The main feature of our method i...
In this paper we propose a meta-modeling approach to adaptive knowledge management. It extends previous work by introducing an application-specific layer which allows to specify m...