Explicit Image Quality Detection Rules for Functional Safety in Computer Vision

Johann Thor Mogensen Ingibergsson, Dirk Kraft, Ulrik Pagh Schultz

2017

Abstract

Computer vision has applications in a wide range of areas from surveillance to safety-critical control of autonomous robots. Despite the potentially critical nature of the applications and a continuous progress, the focus on safety in relation to compliance with standards has been limited. As an example, field robots are typically dependent on a reliable perception system to sense and react to a highly dynamic environment. The perception system thus introduces significant complexity into the safety-critical path of the robotic system. This complexity is often argued to increase safety by improving performance; however, the safety claims are not supported by compliance with any standards. In this paper, we present rules that enable low-level detection of quality problems in images and demonstrate their applicability on an agricultural image database. We hypothesise that low-level and primitive image analysis driven by explicit rules facilitates complying with safety standards, which improves the real-world applicability of existing proposed solutions. The rules are simple independent image analysis operations focused on determining the quality and usability of an image.

Paper Citation

in Harvard Style

Ingibergsson J., Kraft D. and Pagh Schultz U. (2017). Explicit Image Quality Detection Rules for Functional Safety in Computer Vision . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 433-444. DOI: 10.5220/0006125604330444

in Bibtex Style

@conference{visapp17,
author={Johann Thor Mogensen Ingibergsson and Dirk Kraft and Ulrik Pagh Schultz},
title={Explicit Image Quality Detection Rules for Functional Safety in Computer Vision},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={433-444},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006125604330444},
isbn={978-989-758-227-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Explicit Image Quality Detection Rules for Functional Safety in Computer Vision
SN - 978-989-758-227-1
AU - Ingibergsson J.
AU - Kraft D.
AU - Pagh Schultz U.
PY - 2017
SP - 433
EP - 444
DO - 10.5220/0006125604330444