This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for ...
In this paper we present a framework for using multi-layer perceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is h...
Current state-of-the-art methods in variational image segmentation using level set methods are able to robustly segment complex textured images in an unsupervised manner. In recent...
A new variational Maximum A Posteriori (MAP) contextual modeling approach is presented that minimizes the product of two ratios: (a) the ratio of the model distribution to the dis...
One of the key factors for the success of recent energy
minimization methods is that they seek to compute global
solutions. Even for non-convex energy functionals, optimization
...
Petter Strandmark, Fredrik Kahl, Niels Chr. Overga...