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Robust Event-Based Vision Model Estimation by Dispersion Minimisation.

Authors :
Nunes, Urbano Miguel
Demiris, Yiannis
Source :
IEEE Transactions on Pattern Analysis & Machine Intelligence. Dec2022, Vol. 44 Issue Part2, p9561-9573. 13p.
Publication Year :
2022

Abstract

We propose a novel Dispersion Minimisation framework for event-based vision model estimation, with applications to optical flow and high-speed motion estimation. The framework extends previous event-based motion compensation algorithms by avoiding computing an optimisation score based on an explicit image-based representation, which provides three main benefits: i) The framework can be extended to perform incremental estimation, i.e., on an event-by-event basis. ii) Besides purely visual transformations in 2D, the framework can readily use additional information, e.g., by augmenting the events with depth, to estimate the parameters of motion models in higher dimensional spaces. iii) The optimisation complexity only depends on the number of events. We achieve this by modelling the event alignment according to candidate parameters and minimising the resultant dispersion, which is computed by a family of suitable entropy-based measures. Data whitening is also proposed as a simple and effective pre-processing step to make the framework’s accuracy performance more robust, as well as other event-based motion-compensation methods. The framework is evaluated on several challenging motion estimation problems, including 6-DOF transformation, rotational motion, and optical flow estimation, achieving state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01628828
Volume :
44
Issue :
Part2
Database :
Academic Search Index
Journal :
IEEE Transactions on Pattern Analysis & Machine Intelligence
Publication Type :
Academic Journal
Accession number :
160711799
Full Text :
https://doi.org/10.1109/TPAMI.2021.3130049