Uncertainty Comparison of Visual Sensing in Adverse Weather Conditions†
AbstractThis 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
Share & Cite This Article
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.
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(7):1125.Chicago/Turabian Style
Lo, Shi-Wei; Wu, Jyh-Horng; Chen, Lun-Chi; Tseng, Chien-Hao; Lin, Fang-Pang; Hsu, Ching-Han. 2016. "Uncertainty Comparison of Visual Sensing in Adverse Weather Conditions." Sensors 16, no. 7: 1125.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.