The Challenges and Advantages with a Parallel Implementation of Feature Matching

Anders Hast, Andrea Marchetti

Abstract

The number of cores per cpu is predicted to double every second year. Therefore, the opportunity to parallelise currently used algorithms in computer vision and image processing needs to be addressed sooner rather than later. A parallel feature matching approach is proposed and evaluated in Matlab􏰂. The key idea is to use different interest point detectors so that each core can work on its own subset independently of the others. However, since the image pairs are the same, the homography will be essentially the same and can therefore be distributed by the process that first finds a solution. Nevertheless, the speedup is not linear and reasons why is discussed.

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Paper Citation


in Harvard Style

Hast A. and Marchetti A. (2016). The Challenges and Advantages with a Parallel Implementation of Feature Matching . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 101-106. DOI: 10.5220/0005674501010106


in Bibtex Style

@conference{visapp16,
author={Anders Hast and Andrea Marchetti},
title={The Challenges and Advantages with a Parallel Implementation of Feature Matching},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={101-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005674501010106},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - The Challenges and Advantages with a Parallel Implementation of Feature Matching
SN - 978-989-758-175-5
AU - Hast A.
AU - Marchetti A.
PY - 2016
SP - 101
EP - 106
DO - 10.5220/0005674501010106