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Sensors 2016, 16(7), 1125; doi:10.3390/s16071125

Uncertainty Comparison of Visual Sensing in Adverse Weather Conditions

1
National Center for High-Performance Computing, No. 7, R&D 6th Rd., Hsinchu Science Park, Hsinchu 30076, Taiwan
2
Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan
This paper is an extended version of our paper published in Lo, S.-W.; Wu, J.-H.; Chen, L.-C.; Tseng, C.-H.; Lin, F.-P. Flood Tracking in Severe Weather. In Proceedings of the International Symposium on Computer, Consumer and Control, Taichung, Taiwan, 10–12 June 2014; pp. 27–30.
*
Authors to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 28 April 2016 / Revised: 5 July 2016 / Accepted: 15 July 2016 / Published: 20 July 2016
(This article belongs to the Special Issue Imaging: Sensors and Technologies)

Abstract

This paper focuses on flood-region detection using monitoring images. However, adverse weather affects the outcome of image segmentation methods. In this paper, we present an experimental comparison of an outdoor visual sensing system using region-growing methods with two different growing rules—namely, GrowCut and RegGro. For each growing rule, several tests on adverse weather and lens-stained scenes were performed, taking into account and analyzing different weather conditions with the outdoor visual sensing system. The influence of several weather conditions was analyzed, highlighting their effect on the outdoor visual sensing system with different growing rules. Furthermore, experimental errors and uncertainties obtained with the growing rules were compared. The segmentation accuracy of flood regions yielded by the GrowCut, RegGro, and hybrid methods was 75%, 85%, and 87.7%, respectively. View Full-Text
Keywords: vision application; outdoor imaging; visual sensing; flood detection vision application; outdoor imaging; visual sensing; flood detection
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lo, S.-W.; Wu, J.-H.; Chen, L.-C.; Tseng, C.-H.; Lin, F.-P.; Hsu, C.-H. Uncertainty Comparison of Visual Sensing in Adverse Weather Conditions. Sensors 2016, 16, 1125.

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