{"title":"Fog detection and visibility enhancement under partial machine learning approach","authors":"C. Lakshmi, D. Rao, G. Rao","doi":"10.1109/ICPCSI.2017.8391898","DOIUrl":null,"url":null,"abstract":"Fog detection and evaluation in critical conditions is a major challenge in current times. In this paper a modernized semi-automated machine learning technique for fog detection under the given scenario of back-veil scattering technique is discussed and evaluated. The observative research presents the overall scenario of detecting and analyzing the given input video, acquired and processed with faster capturing and the expected results are archived with experimental observations and comparative study. On a whole, the system is efficient in understanding and analyzing the visibility intensity and obstacle detection.","PeriodicalId":6589,"journal":{"name":"2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)","volume":"9 1","pages":"1192-1194"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPCSI.2017.8391898","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Fog detection and evaluation in critical conditions is a major challenge in current times. In this paper a modernized semi-automated machine learning technique for fog detection under the given scenario of back-veil scattering technique is discussed and evaluated. The observative research presents the overall scenario of detecting and analyzing the given input video, acquired and processed with faster capturing and the expected results are archived with experimental observations and comparative study. On a whole, the system is efficient in understanding and analyzing the visibility intensity and obstacle detection.