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Massively Parallel Optical Flow using Distributed Local Search

Authors:
Abdelkhalek Mansouri
Jean-Charles Creput
Fabrice Lauri
Hongjian Wang

Keywords: Optical flow; Parallel and distributed computing; Variable neighborhood search; Graphics processing unit

Abstract:
The design of many tasks in computer vision field requires addressing difficult NP-hard energy optimization problems. An example of application is the visual correspondence problem of optical flow, which can be formulated as an elastic pattern matching optimization problem. Pixels of a first image have to be matched to pixels in a second image while preserving elastic smoothness constraint on the first image deformation. In this paper, we present a parallel approach to address optical flow problem following the concept of distributed local search. Distributed local search consists in the parallel execution of many standard local search processes operating on a partition of the data. Each process performs local search on its own part of the data such that the overall energy is minimized. The approach is implemented on graphics processing unit (GPU) platform and evaluated on standard Middlebury benchmarks to gauge the substantial acceleration factors that can be achieved in the task of energy minimization.

Pages: 31 to 36

Copyright: Copyright (c) IARIA, 2018

Publication date: February 18, 2018

Published in: conference

ISSN: 2308-3557

ISBN: 978-1-61208-612-5

Location: Barcelona, Spain

Dates: from February 18, 2018 to February 22, 2018