Robust Interest Point Detection by Local Zernike Moments

Gökhan Özbulak, Muhittin Gökmen

Abstract

In this paper, a novel interest point detector based on Local Zernike Moments is presented. Proposed detector, which is named as Robust Local Zernike Moment based Features (R-LZMF), is invariant to scale, rotation and translation changes in images and this makes it robust when detecting interesting points across the images that are taken from same scene under varying view conditions such as zoom in/out or rotation. As our experiments on the Inria Dataset indicate, R-LZMF outperforms widely used detectors such as SIFT and SURF in terms of repeatability that is main criterion for evaluating detector performance.

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Paper Citation


in Harvard Style

Özbulak G. and Gökmen M. (2015). Robust Interest Point Detection by Local Zernike Moments . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 644-651. DOI: 10.5220/0005343506440651


in Bibtex Style

@conference{visapp15,
author={Gökhan Özbulak and Muhittin Gökmen},
title={Robust Interest Point Detection by Local Zernike Moments},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={644-651},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005343506440651},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Robust Interest Point Detection by Local Zernike Moments
SN - 978-989-758-089-5
AU - Özbulak G.
AU - Gökmen M.
PY - 2015
SP - 644
EP - 651
DO - 10.5220/0005343506440651