[1]陈立伟,倪杰.基于多阈值模糊增强的手指静脉图像分割[J].应用科技,2011,38(04):14-18.[doi:doi:10.3969/j.issn.1009-671X.2011.04.04]
 CHEN Liwei,NI Jie.Method for finger vein feature segmentation based on multithreshold fuzzy enhancement[J].Applied science and technology,2011,38(04):14-18.[doi:doi:10.3969/j.issn.1009-671X.2011.04.04]
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基于多阈值模糊增强的手指静脉图像分割(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第38卷
期数:
2011年04期
页码:
14-18
栏目:
现代电子技术
出版日期:
2011-04-05

文章信息/Info

Title:
Method for finger vein feature segmentation based on multithreshold fuzzy enhancement
文章编号:
1009-671X (2011) 04-0014-05
作者:
陈立伟倪杰
哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
CHEN Liwei NI Jie
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
手指静脉特征提取模糊增强图像分割
Keywords:
finger vein feature extraction fuzzy enhancement image segmentation
分类号:
TP911.7
DOI:
doi:10.3969/j.issn.1009-671X.2011.04.04
文献标志码:
A
摘要:
根据手指静脉图像的特点,针对经典阈值方法难以满足图像多属性分割要求的不足,文章在单层次模糊图像增强算法的基础上,提出了一种改进的手指静脉图像分割算法,讨论了该算法的基本原理和具体计算步骤.实验结果表明:与传统的方法相比,该算法方法简单、容易实现,对低质量手指静脉的分割达到了令人满意的效果,分割结果不但准确而且纹路具有明显方向性.
Abstract:
The classical method of image threshold segmentation is difficult to meet the requirement of multiattribute segmentation. Based on the features of the finger vein image and the sheer level fuzzy image enhancement algorithm, an enhanced method is proposed for extracting finger vein features, and the fundamental theory and concrete calculation steps are discussed. The experimental results show that the proposed image segmentation method is simple, easy to be achieved, comparing with traditional feature extraction algorithm. The effect of extracting finger vein feature is satisfactory, and the extracted feature is accurate. Segmentation results have accurate orientation and the grain is obvious.

参考文献/References:

[1]孙东梅,裘正定.生物特征识别技术综述[J].电子学报,2001,29(12A):1744-1746.
[2] 王科俊,丁宇航,庄大燕,等.手背静脉图像阈值分割[J].自动化技术与应用,2005,6(8):22-25.
[3] NAOTO M. AKIO N, TAKAFUMI M,Extraction of finger vein patterns using maximum curvature points in image profiles[J].IEICE Transactions on Information and Systems,2007,E90-D(8):1185-1194.
[4] NAOTO M,AKIO N,TAKAFUMI M.Feature extraction of fingervein patterns based on repeated line tracking and its application to personal identification.Machine Vision and Applications[J].Digital Object Identifer, 2004, 15(4): 149-152.
[5] KOVACS V Z M,ROVATTI R,FRAZZONI M.Fingerprint ridge distance computation methodologies[J].Pattern Recognition,2000,33:69-80.
[6] 何斌,马天予.Visual C〖KG-*2〗+〖KG-*2〗+数字图像处理[M].北京:人民邮电出版社,2003.
[7] 余成波,秦华锋.手指静脉识别技术[M].北京:清华大学出版社,2009:125-214.
[8] 鱼海涛,邵小强.基于一种新模糊增强算子的图像边缘检测算法[J].西安科技大学学报,2008,28(1):194-196.
[9]FEIXAS R J,SBERT M.metal Medical image segmentation based on mutual information maximization[C]// Proceedings of MICCAI2004.Saint Malo,France,2004:135-142.

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备注/Memo

备注/Memo:
国家自然科学基金资助项目(60702053)
更新日期/Last Update: 2011-06-10