Gaël Mondonneix, S. Chabrier, Jean-Martial Mari, A. Gabillon
{"title":"塔希提珍珠光泽评估自动化","authors":"Gaël Mondonneix, S. Chabrier, Jean-Martial Mari, A. Gabillon","doi":"10.1109/AIPR.2017.8457974","DOIUrl":null,"url":null,"abstract":"Luster assessment stands at the crossroads of different fields and there is very few literature specifically dedicated to it. In a perspective of automating culture pearls' luster assessment, a way to extract features out of pearls' photographs is proposed and tested on a real dataset labeled by a human expert. After training, an SVM using these features can predict luster quality of new pearls with up to 87.3 % (± 5.7) accuracy. Moreover, it turns out that some of these features could be used for developing an objective luster quality control.","PeriodicalId":128779,"journal":{"name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Tahitian Pearls' Luster Assessment Automation\",\"authors\":\"Gaël Mondonneix, S. Chabrier, Jean-Martial Mari, A. Gabillon\",\"doi\":\"10.1109/AIPR.2017.8457974\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Luster assessment stands at the crossroads of different fields and there is very few literature specifically dedicated to it. In a perspective of automating culture pearls' luster assessment, a way to extract features out of pearls' photographs is proposed and tested on a real dataset labeled by a human expert. After training, an SVM using these features can predict luster quality of new pearls with up to 87.3 % (± 5.7) accuracy. Moreover, it turns out that some of these features could be used for developing an objective luster quality control.\",\"PeriodicalId\":128779,\"journal\":{\"name\":\"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIPR.2017.8457974\",\"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 Applied Imagery Pattern Recognition Workshop (AIPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2017.8457974","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Luster assessment stands at the crossroads of different fields and there is very few literature specifically dedicated to it. In a perspective of automating culture pearls' luster assessment, a way to extract features out of pearls' photographs is proposed and tested on a real dataset labeled by a human expert. After training, an SVM using these features can predict luster quality of new pearls with up to 87.3 % (± 5.7) accuracy. Moreover, it turns out that some of these features could be used for developing an objective luster quality control.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
