Sampling based Bundle Adjustment using Feature Matches between Ground-view and Aerial Images

Hideyuki Kume, Tomokazu Sato, Naokazu Yokoya

2014

Abstract

This paper proposes a new pipeline of Structure-from-Motion that uses feature matches between ground- view and aerial images for removing accumulative errors. In order to find good matches from unreliable matches, we newly propose RANSAC based outlier elimination methods in both feature matching and bundle adjustment stages. To this end, in the feature matching stage, the consistency of orientation and scale extracted from images by a feature descriptor is checked. In the bundle adjustment stage, we focus on the consistency between estimated geometry and matches. In experiments, we quantitatively evaluate performances of the proposed feature matching and bundle adjustment.

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Paper Citation


in Harvard Style

Kume H., Sato T. and Yokoya N. (2014). Sampling based Bundle Adjustment using Feature Matches between Ground-view and Aerial Images . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 692-698. DOI: 10.5220/0004850306920698


in Bibtex Style

@conference{visapp14,
author={Hideyuki Kume and Tomokazu Sato and Naokazu Yokoya},
title={Sampling based Bundle Adjustment using Feature Matches between Ground-view and Aerial Images},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={692-698},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004850306920698},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Sampling based Bundle Adjustment using Feature Matches between Ground-view and Aerial Images
SN - 978-989-758-009-3
AU - Kume H.
AU - Sato T.
AU - Yokoya N.
PY - 2014
SP - 692
EP - 698
DO - 10.5220/0004850306920698