Explicit Image Quality Detection Rules for Functional Safety in Computer Vision

Johann Thor Mogensen Ingibergsson, Dirk Kraft, Ulrik Pagh Schultz

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.

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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