Georgios Papamakarios

Learn More
The robust estimation of the low-dimensional subspace that spans the data from a set of high-dimensional, possibly corrupted by gross errors and outliers observations is fundamental in many computer vision problems. The state-of-the-art robust principal component analysis (PCA) methods adopt convex relaxations of 0 quasi-norm-regularised rank minimisation(More)
In-house automatic activity detection is highly important toward the automatic evaluation of the resident's cognitive state. However, current activity detection systems suffer from the demand for on-site acquisition of large amounts of ground truth data for training purposes, which poses a major obstacle to their real-world applicability. In this paper,(More)
This paper presents a tool to support and monitor the execution of common physical exercise interventions targeting people with Mild Cognitive Impairment (MCI), Alzheimer's disease (AD) and elderly in general. Our tool aims (a) to stimulate and guide patients within physical exercise programs , (b) to monitor patient capacity to perform exercises suggested(More)
Robust low-rank modelling has recently emerged as a family of powerful methods for recovering the low-dimensional structure of grossly corrupted data, and has become successful in a wide range of applications in signal processing and computer vision. In principle, robust low-rank modelling focuses on decomposing a given data matrix into a low-rank and a(More)
  • 1