计算机与现代化

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 基于CLAHE和改进的NIBLACK算法的手背静脉提取方法

  

  1. 平顶山学院计算机科学与技术学院,河南平顶山467000
  • 收稿日期:2014-12-24 出版日期:2015-04-27 发布日期:2015-04-29
  • 作者简介: 喻恒(1984-),男,河南平顶山人,平顶山学院计算机科学与技术学院助教,硕士,研究方向:视觉物联网,模式识别; 郑均辉(1981-),男,四川叙永人,讲师,硕士,研究方向 :人工智能,算法分析。
  • 基金资助:
    河南省科技攻关基金资助项目(132002210443)

Hand Vein Image Extraction Method #br#  Based on CLAHE and Improved NIBLACK Algorithm

  1. College of Computer Science and Technology, Pingdingshan University, Pingdingshan 467000, China
  • Received:2014-12-24 Online:2015-04-27 Published:2015-04-29

摘要:

 为了满足手背静脉识别对于特征提取的需求,本文对手背静脉提取方法进行了研究。首先采用CLAHE算法对手背静脉图像进行增强处理,然后对增强图像进行二值化处理。针对传统的
NIBALCK二值化算法容易产生噪声、断纹以及分块处理时的边缘阈值缺失的问题,提出一种局部静态阈值与NIBLACK相结合的改进算法。实验结果表明,该方法可以有效地克服光强因素对图像提取的影响
,消除传统方法的噪声过多、纹络断裂的现象,保持完整清晰的静脉纹络结构,满足后续识别工作的需要。

关键词:  , 手背静脉, 图像增强, CLAHE算法, NIBLACK算法

Abstract:

In order to meet the demand for the feature extraction of hand vein recognition, this paper takes a study on hand vein extraction method. Firstly, before the
image binaryzation processing, By using CLAHE algorithm the hand vein images is enhanced. Because of the traditional NIBLACK algorithm’s problems such as noises, broken lines
and edge threshold missing and so on, we propose an improved algorithm which combined local static threshold method with the NIBLACK algorithm to segment the vein image.
Experimental results show that the methods of CLAHE and improved NIBLACK algorithm can effectively overcome the impact of the intensity on image extraction, and eliminate the
problems of noise and broken lines. So we can obtain original and clear vein structure, which meets the needs of hand vein recognition.

Key words: hand vein, image enhancement, CLAHE algorithm, NIBLACK algorithm