Detection of Low-textured Objects

Christopher Bulla, Andreas Weissenburger

2015

Abstract

In this paper, we present a descriptor architecture, SIFText, that combines texture, shape and color information in one descriptor. The respective descriptor parts are weighted according to the underlying image content, thus we are able to detect and locate low-textured objects in images without performance losses for textured objects. We furthermore present a matching strategy beside the frequently used nearest neighbor matching that has been especially designed for the proposed descriptor. Experiments on synthetically generated images show the improvement of our descriptor in comparison to the standard SIFT descriptor. We show that we are able to detect more features in non-textured regions, which facilitates an accurate detection of non-textured objects. We further show that the performance of our descriptor is comparable to the performance of the SIFT descriptor for textured objects.

Paper Citation

in Harvard Style

Bulla C. and Weissenburger A. (2015). Detection of Low-textured Objects . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 265-273. DOI: 10.5220/0005269602650273

in Bibtex Style

@conference{visapp15,
author={Christopher Bulla and Andreas Weissenburger},
title={Detection of Low-textured Objects},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={265-273},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005269602650273},
isbn={978-989-758-090-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Detection of Low-textured Objects
SN - 978-989-758-090-1
AU - Bulla C.
AU - Weissenburger A.
PY - 2015
SP - 265
EP - 273
DO - 10.5220/0005269602650273