{"title":"基于视觉的局部二值模式-三个正交平面火灾探测","authors":"F. Sthevanie, H. Nugroho, F. A. Yulianto","doi":"10.1109/CYBERNETICSCOM.2013.6865801","DOIUrl":null,"url":null,"abstract":"This paper discussed a proposed method to detect fire with the main focus is to extract the fire features to increase the accuracy and using LBP-TOP feature extraction scheme to accelerate the process. The fire features produced by LBP-TOP was modeled by using dustering K-Means method as the reference model when the classification process was done by using K-NN method. By using those methods, the accuracy of the detection process can reach 92% and the computational cost can be reduced.","PeriodicalId":351051,"journal":{"name":"2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Visual-based fire detection using local binary pattern-three orthogonal planes\",\"authors\":\"F. Sthevanie, H. Nugroho, F. A. Yulianto\",\"doi\":\"10.1109/CYBERNETICSCOM.2013.6865801\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discussed a proposed method to detect fire with the main focus is to extract the fire features to increase the accuracy and using LBP-TOP feature extraction scheme to accelerate the process. The fire features produced by LBP-TOP was modeled by using dustering K-Means method as the reference model when the classification process was done by using K-NN method. By using those methods, the accuracy of the detection process can reach 92% and the computational cost can be reduced.\",\"PeriodicalId\":351051,\"journal\":{\"name\":\"2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM)\",\"volume\":\"150 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CYBERNETICSCOM.2013.6865801\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBERNETICSCOM.2013.6865801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visual-based fire detection using local binary pattern-three orthogonal planes
This paper discussed a proposed method to detect fire with the main focus is to extract the fire features to increase the accuracy and using LBP-TOP feature extraction scheme to accelerate the process. The fire features produced by LBP-TOP was modeled by using dustering K-Means method as the reference model when the classification process was done by using K-NN method. By using those methods, the accuracy of the detection process can reach 92% and the computational cost can be reduced.