In this paper, we consider the problem of geolocating an unknown emitter by a satellite cluster. We formulate the problem as the maximum likelihood location estimation by using TD...
We derive a convex relaxation for cardinality constrained Principal Component Analysis (PCA) by using a simple representation of the L1 unit ball and standard Lagrangian duality. ...
This paper considers recovery of jointly sparse multichannel signals from incomplete measurements. Several approaches have been developed to recover the unknown sparse vectors from...
In this work we revisit the Mumford-Shah functional, one of the most studied variational approaches to image segmentation. The contribution of this paper is to propose an algorith...
Thomas Pock, Daniel Cremers, Horst Bischof, Antoni...
Approximate MAP inference in graphical models is an important and challenging problem for many domains including computer vision, computational biology and natural language unders...
We investigate a new, convex relaxation of an expectation-maximization (EM) variant that approximates a standard objective while eliminating local minima. First, a cautionary resu...
Convex relaxations for continuous multilabel problems have attracted a lot of interest recently [1–5]. Unfortunately, in previous methods, the runtime and memory requirements sca...
We introduce a convex relaxation framework to optimally
minimize continuous surface ratios. The key idea is to minimize
the continuous surface ratio by solving a sequence
of con...
Kalin Kolev (University of Bonn), Daniel Cremers (...