{"title":"相一致性图像与稀疏分类器用于新生儿疼痛状态分类","authors":"M. N. Mansor, Mohd Nazri Rejab","doi":"10.1109/ICCSCE.2013.6720007","DOIUrl":null,"url":null,"abstract":"Most of infant pain cause changes in the face. Clinicians use image analysis to characterize the pathological faces. Nowadays, infant pain research is increasing dramatically due to high demand from all medical team. This paper presents a sparse and naïve Bayes classifier for the diagnosis of infant pain disorders. Phase congruency image and local binary pattern are proposed. The proposed algorithms provide very promising classification rate.","PeriodicalId":319285,"journal":{"name":"2013 IEEE International Conference on Control System, Computing and Engineering","volume":"30 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Phase congruency image and sparse classifier for newborn classifying pain state\",\"authors\":\"M. N. Mansor, Mohd Nazri Rejab\",\"doi\":\"10.1109/ICCSCE.2013.6720007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most of infant pain cause changes in the face. Clinicians use image analysis to characterize the pathological faces. Nowadays, infant pain research is increasing dramatically due to high demand from all medical team. This paper presents a sparse and naïve Bayes classifier for the diagnosis of infant pain disorders. Phase congruency image and local binary pattern are proposed. The proposed algorithms provide very promising classification rate.\",\"PeriodicalId\":319285,\"journal\":{\"name\":\"2013 IEEE International Conference on Control System, Computing and Engineering\",\"volume\":\"30 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Control System, Computing and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSCE.2013.6720007\",\"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 Control System, Computing and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSCE.2013.6720007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Phase congruency image and sparse classifier for newborn classifying pain state
Most of infant pain cause changes in the face. Clinicians use image analysis to characterize the pathological faces. Nowadays, infant pain research is increasing dramatically due to high demand from all medical team. This paper presents a sparse and naïve Bayes classifier for the diagnosis of infant pain disorders. Phase congruency image and local binary pattern are proposed. The proposed algorithms provide very promising classification rate.
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