{"title":"基于部分机器学习方法的雾检测与能见度增强","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":"{\"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}","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}
Fog detection and visibility enhancement under partial machine learning approach
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.