{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T23:07:23Z","timestamp":1775776043573,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,7,20]],"date-time":"2016-07-20T00:00:00Z","timestamp":1468972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2014namely, 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.<\/jats:p>","DOI":"10.3390\/s16071125","type":"journal-article","created":{"date-parts":[[2016,7,20]],"date-time":"2016-07-20T09:45:15Z","timestamp":1469007915000},"page":"1125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Uncertainty Comparison of Visual Sensing in Adverse Weather Conditions"],"prefix":"10.3390","volume":"16","author":[{"given":"Shi-Wei","family":"Lo","sequence":"first","affiliation":[{"name":"National Center for High-Performance Computing, No. 7, R&D 6th Rd., Hsinchu Science Park, Hsinchu 30076, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jyh-Horng","family":"Wu","sequence":"additional","affiliation":[{"name":"National Center for High-Performance Computing, No. 7, R&D 6th Rd., Hsinchu Science Park, Hsinchu 30076, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lun-Chi","family":"Chen","sequence":"additional","affiliation":[{"name":"National Center for High-Performance Computing, No. 7, R&D 6th Rd., Hsinchu Science Park, Hsinchu 30076, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chien-Hao","family":"Tseng","sequence":"additional","affiliation":[{"name":"National Center for High-Performance Computing, No. 7, R&D 6th Rd., Hsinchu Science Park, Hsinchu 30076, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang-Pang","family":"Lin","sequence":"additional","affiliation":[{"name":"National Center for High-Performance Computing, No. 7, R&D 6th Rd., Hsinchu Science Park, Hsinchu 30076, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2351-2052","authenticated-orcid":false,"given":"Ching-Han","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0013-7952(00)00058-2","article-title":"Characteristics of hazards induced by extremely heavy rainfall in Central Taiwan\u2014Typhoon Herb","volume":"58","author":"Lin","year":"2000","journal-title":"Eng. 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