Inference tasks in Markov random fields (MRFs) are closely related to the constraint satisfaction problem (CSP) and its soft generalizations. In particular, MAP inference in MRF is equivalent to the weighted (maxsum) CSP. ...

The max-sum labeling problem, de ned as maximizing a sum of functions of pairs of discrete variables, is a general optimization problem with numerous applications, e.g., computing MAP assignments of a Markov random eld. ...

The existing view on loopy belief propagation sees it as an algorithm to nd a common zero of a system of non-linear functions, not explicitly related to each other. We show that these functions are in fact related { they ...

It is known that epipolar constraint can be augmented with orientation by formulating it in the oriented projective geometry. This oriented epipolar constraint requires knowing the orientations (signs of overall scales) ...

We describe progress in automatically fitting a plane plus modelled perturbation surface model to represent architectural scenes. There are two areas of novelty. The first is a method of fitting parametrized models in which ...

We generalise the linear programming relaxation approach to Weighted CSP by Schlesinger and the max-sum diffusion algorithm by Koval and Kovalevsky twice: from Weighted CSP to Semiring CSP, and from binary networks to ...

It is known that xed points of loopy belief propagation (BP) correspond to stationary points of the Bethe variational problem, where we minimize the Bethe free energy subject to normalization and marginalization constraints. ...

Constraint Satisfaction Problem (CSP), including its soft modifications, is ubiquitous in artificial intelligence and related fields. In computer vision and pattern recognition, the crisp CSP is more known as the consistent ...