Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers

We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or in short as IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by use of a coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with classic computer vision related MRF problems, where our novel framework constantly yields a significant time speed-up (with respect to the most efficient inference methods) and obtains a more accurate solution than directly optimizing the MRF. We make our code available on-line [4]

A multigrid platform for real-time motion computation with discontinuity-preserving variational methods

Variational methods are among the most accurate techniques for estimating the optic flow. They yield dense flow fields and can be designed such that they preserve discontinuities, allow to deal with large displacements and perform well under noise or varying illumination. However, such adaptations render the minimisation of the underlying energy functional very expensive in terms of computational costs: Typically, one or more large linear or nonlinear systems of equations have to be solved in order to obtain the desired solution. Consequently, variational methods are considered to be too slow for real-time performance. In our paper we address this problem in two ways: (i) We present a numerical framework based on bidirectional multigrid methods for accelerating a broad class of variational optic flow methods with different constancy and smoothness assumptions. In particular, discontinuity-preserving regularisation strategies are thereby in the focus of our work. (ii) We show by the examples of classical as well as more advanced variational techniques that real-time performance is possible - even for very complex optic flow models with high accuracy. Experiments show frame rates up to 63 dense flow fields per second for real-world image sequences of size 160 x 120 on a standard PC. Compared to classical iterative methods this constitutes a speedup of two to four orders of magnitude

Forschungsbericht Universität Mannheim, 2004 / 2005

Die Universität Mannheim gibt in dem vorliegenden Forschungsbericht 2004/2005 Rechenschaft über ihre Leistungen auf dem Gebiet der Forschung. Erstmals folgt diese Dokumentation einer neuen Gliederung, die auf einen Beschluss des Forschungsrates der Universität Mannheim zurückgeht. Wie gewohnt erhalten Sie einen Überblick über die Publikationen und Forschungsprojekte der Lehrstühle, Professuren und zentralen Forschungseinrichtungen. Diese werden ergänzt um Angaben zur Organisation von Forschungsveranstaltungen, der Mitwirkung in Forschungsausschüssen, einer Übersicht zu den für Forschungszwecke eingeworbenen Drittmitteln, zu den Promotionen und Habilitationen, zu Preisen und Ehrungen und zu Förderern der Universität Mannheim. Abgerundet werden diese Daten durch zusammenfassende Darstellungen der Forschungsschwerpunkte und des Forschungsprofils der Fakultäten