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

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flowcomputation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we develop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow computation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation

Sichere Mensch-Roboter-Kooperation durch Auswertung von Bildfolgen

Eine Herausforderung ist die Integration von Sensoren in die Roboterzelle und die Implementierung der dazugehörigen Auswerteverfahren. Durch die Integration von Sensorsystemen soll es den Manipulatoren ermöglicht werden, weitestgehend autonom Probleme zu lösen. Ein wichtiger Trend in der Forschung und Entwicklung komplexer Robotersysteme liegt darin, eine zuverlässige kognitive Industrierobotik zu entwickeln. Diese Arbeit will dazu einen wichtigen Beitrag leisten

FRIEDRICH-ALEXANDER-UNIVERSITÄT ERLANGEN-NÜRNBERG INSTITUT FÜR INFORMATIK (MATHEMATISCHE MASCHINEN UND DATENVERARBEITUNG) Lehrstuhl für Informatik 10 (Systemsimulation) A variational multigrid for computing the optical flow

Computing the optical flow for a sequence of images is currently a standard low-level problem in machine vision. A classical way to solve this problem is the Horn-Schunck algorithm. It corresponds to a coupled Gauss-Seidel relaxation for solving a system of two PDEs. The convergence of the algorithm is in general poor. A multigrid strategy can be expected to provide a significant acceleration. We investigate in this paper a V-cycle multigrid implementation based on the Galerkin approach. Experiments on synthetic and natural images show that the method provides a significant performance improvement. Key words. Optical flow, Horn-Schunck algorithm, multigrid, Galerkin discretization.