{"title":"手指分割及其逼近","authors":"Pavel Jetenský, J. Marek, J. Rak","doi":"10.1109/RADIOELEK.2015.7129026","DOIUrl":null,"url":null,"abstract":"Aim of the paper is to find an algorithm for detecting finger's knuckle in the binary image with high precision and accuracy. At our disposal are measured coordinates of points on the finger, which are obtained from Microsoft Kinect. We form a suitable model for finger's characterization. It is based on the transformation of coordinates and regression models. A regression model gives a non-smooth function with change point. The main goal is to find the change point of the approximation function corresponding to the knuckle.","PeriodicalId":193275,"journal":{"name":"2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Fingers segmentation and its approximation\",\"authors\":\"Pavel Jetenský, J. Marek, J. Rak\",\"doi\":\"10.1109/RADIOELEK.2015.7129026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aim of the paper is to find an algorithm for detecting finger's knuckle in the binary image with high precision and accuracy. At our disposal are measured coordinates of points on the finger, which are obtained from Microsoft Kinect. We form a suitable model for finger's characterization. It is based on the transformation of coordinates and regression models. A regression model gives a non-smooth function with change point. The main goal is to find the change point of the approximation function corresponding to the knuckle.\",\"PeriodicalId\":193275,\"journal\":{\"name\":\"2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA)\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RADIOELEK.2015.7129026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RADIOELEK.2015.7129026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Aim of the paper is to find an algorithm for detecting finger's knuckle in the binary image with high precision and accuracy. At our disposal are measured coordinates of points on the finger, which are obtained from Microsoft Kinect. We form a suitable model for finger's characterization. It is based on the transformation of coordinates and regression models. A regression model gives a non-smooth function with change point. The main goal is to find the change point of the approximation function corresponding to the knuckle.
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