Wandi Du, Hanshi Wang, Lizhen Liu, Wei Song, Jingli Lu
{"title":"基于稀疏表示的指纹分割优化","authors":"Wandi Du, Hanshi Wang, Lizhen Liu, Wei Song, Jingli Lu","doi":"10.1109/ICEDIF.2015.7280165","DOIUrl":null,"url":null,"abstract":"Fingerprint segmentation is the key step of fingerprint image preprocessing. Efficient fingerprint segmentation technology has significance in both saving preprocessing time and improving the image quality. In this paper, on the basis of the right direction of the fingerprint ridge, we use the gradient threshold method to segment image for the first time. While there are still limitations on the performance of the first segmentation, the second segmentation is used to improve the quality of results, which is based on matrix manipulation. Experimental results prove that this method has better denoising performance and higher computing speed. Finally, we get the high-quality fingerprint image.","PeriodicalId":355975,"journal":{"name":"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The optimization of fingerprint segmentation based on sparse representation\",\"authors\":\"Wandi Du, Hanshi Wang, Lizhen Liu, Wei Song, Jingli Lu\",\"doi\":\"10.1109/ICEDIF.2015.7280165\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fingerprint segmentation is the key step of fingerprint image preprocessing. Efficient fingerprint segmentation technology has significance in both saving preprocessing time and improving the image quality. In this paper, on the basis of the right direction of the fingerprint ridge, we use the gradient threshold method to segment image for the first time. While there are still limitations on the performance of the first segmentation, the second segmentation is used to improve the quality of results, which is based on matrix manipulation. Experimental results prove that this method has better denoising performance and higher computing speed. Finally, we get the high-quality fingerprint image.\",\"PeriodicalId\":355975,\"journal\":{\"name\":\"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)\",\"volume\":\"97 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEDIF.2015.7280165\",\"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 International Conference on Estimation, Detection and Information Fusion (ICEDIF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEDIF.2015.7280165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The optimization of fingerprint segmentation based on sparse representation
Fingerprint segmentation is the key step of fingerprint image preprocessing. Efficient fingerprint segmentation technology has significance in both saving preprocessing time and improving the image quality. In this paper, on the basis of the right direction of the fingerprint ridge, we use the gradient threshold method to segment image for the first time. While there are still limitations on the performance of the first segmentation, the second segmentation is used to improve the quality of results, which is based on matrix manipulation. Experimental results prove that this method has better denoising performance and higher computing speed. Finally, we get the high-quality fingerprint image.