Robust Guided Matching and Multi-layer Feature Detection Applied to High Resolution Spherical Images

Christiano Couto Gava, Alain Pagani, Bernd Krolla, Didier Stricker

Abstract

We present a novel, robust guided matching technique. Given a set of calibrated spherical images along with the associated sparse 3D point cloud, our approach consistently finds matches across the images in a multilayer feature detection framework. New feature matches are used to refine existing 3D points or to add reliable ones to the point cloud, therefore improving scene representation. We use real indoor and outdoor scenarios to validate the robustness of the proposed approach. Moreover, we perform a quantitative evaluation of our technique to demonstrate its effectiveness.

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Paper Citation


in Harvard Style

Gava C., Pagani A., Krolla B. and Stricker D. (2013). Robust Guided Matching and Multi-layer Feature Detection Applied to High Resolution Spherical Images . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 322-327. DOI: 10.5220/0004300603220327


in Bibtex Style

@conference{visapp13,
author={Christiano Couto Gava and Alain Pagani and Bernd Krolla and Didier Stricker},
title={Robust Guided Matching and Multi-layer Feature Detection Applied to High Resolution Spherical Images},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={322-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004300603220327},
isbn={978-989-8565-48-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Robust Guided Matching and Multi-layer Feature Detection Applied to High Resolution Spherical Images
SN - 978-989-8565-48-8
AU - Gava C.
AU - Pagani A.
AU - Krolla B.
AU - Stricker D.
PY - 2013
SP - 322
EP - 327
DO - 10.5220/0004300603220327