{"title":"基于图形模型的眼特征点跟踪","authors":"S. Coşar, M. Qetin","doi":"10.1109/SIU.2007.4298738","DOIUrl":null,"url":null,"abstract":"In this paper, a statistical method for eye feature point tracking is proposed. The aim is to track feature points even when the observed data are uncertain because of noise and/or occlusion. With this motivation, a graphical model that uses the spatial information as well as the temporal information between points is built. The proposed method is applied on 2D grayscale real video sequences as a real data application.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Eye Feature Point Tracking by Using Graphical Models\",\"authors\":\"S. Coşar, M. Qetin\",\"doi\":\"10.1109/SIU.2007.4298738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a statistical method for eye feature point tracking is proposed. The aim is to track feature points even when the observed data are uncertain because of noise and/or occlusion. With this motivation, a graphical model that uses the spatial information as well as the temporal information between points is built. The proposed method is applied on 2D grayscale real video sequences as a real data application.\",\"PeriodicalId\":315147,\"journal\":{\"name\":\"2007 IEEE 15th Signal Processing and Communications Applications\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE 15th Signal Processing and Communications Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2007.4298738\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE 15th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2007.4298738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Eye Feature Point Tracking by Using Graphical Models
In this paper, a statistical method for eye feature point tracking is proposed. The aim is to track feature points even when the observed data are uncertain because of noise and/or occlusion. With this motivation, a graphical model that uses the spatial information as well as the temporal information between points is built. The proposed method is applied on 2D grayscale real video sequences as a real data application.
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