Hung-Chung Li, P. Sun, Wei-Chih Su, Hung-Shing Chen, Chia-Pin Chueh, Yennun Huang
{"title":"基于机器学习的荧光样品感知颜色外观模型","authors":"Hung-Chung Li, P. Sun, Wei-Chih Su, Hung-Shing Chen, Chia-Pin Chueh, Yennun Huang","doi":"10.1109/OECC48412.2020.9273718","DOIUrl":null,"url":null,"abstract":"Two machine learning models, including the polynomial regression model and artificial neural network, are proposed based on the result of a visual perception experiment of fluorescent samples. The results indicate that both two models can ideally predict the visual color appearance of fluorescent samples under various lighting conditions with high R-squared, acceptable RMSE, and MAE values.","PeriodicalId":433309,"journal":{"name":"2020 Opto-Electronics and Communications Conference (OECC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Perceptual Color Appearance Models of Fluorescent Samples Based on Machine Learning\",\"authors\":\"Hung-Chung Li, P. Sun, Wei-Chih Su, Hung-Shing Chen, Chia-Pin Chueh, Yennun Huang\",\"doi\":\"10.1109/OECC48412.2020.9273718\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Two machine learning models, including the polynomial regression model and artificial neural network, are proposed based on the result of a visual perception experiment of fluorescent samples. The results indicate that both two models can ideally predict the visual color appearance of fluorescent samples under various lighting conditions with high R-squared, acceptable RMSE, and MAE values.\",\"PeriodicalId\":433309,\"journal\":{\"name\":\"2020 Opto-Electronics and Communications Conference (OECC)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Opto-Electronics and Communications Conference (OECC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OECC48412.2020.9273718\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Opto-Electronics and Communications Conference (OECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OECC48412.2020.9273718","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Perceptual Color Appearance Models of Fluorescent Samples Based on Machine Learning
Two machine learning models, including the polynomial regression model and artificial neural network, are proposed based on the result of a visual perception experiment of fluorescent samples. The results indicate that both two models can ideally predict the visual color appearance of fluorescent samples under various lighting conditions with high R-squared, acceptable RMSE, and MAE values.