自适应模糊阈值法matlab代码-Finger_Vein_Matching:Finger_Vein_Matching
自适应模糊阈值法matlab代码Finger_Vein_Matching
该项目主要研究数字静脉识别技术，包括指静脉图像的采集，图像预处理，特征信息的提取和匹配，以实现指静脉的识别。
介绍
首先，指静脉图像是由实验室中的设备收集的。
由于采集方法的限制，采集的手指静脉图像不可避免地会产生各种噪声。
因此，应对采集的图像进行预处理以尽可能减少引入的噪声。常见的预处理操作包括图像去噪，图像增强，关注区域（ROI）捕获等。对预处理后的图像进行特征提取，主要分为基于纹理的特征提取，基于编码的特征提取和基于细节点的特征提取三类，提取的特征与特征数据库中的模板匹配，如果匹配得分较大超过设定的阈值时，输入图像和模板将被归为同一类别。
否则，将其判断为不相同的类别，从而实现对指静脉的识别。
系统
指静脉识别系统主要包括四个模块：图像采集，图像预处理，特征提取和特征匹配，下面将对这四个模块进行详细介绍。
图像采集模块（指纹成像原理）手指静脉成像的基本原理是利用静脉中红细胞的特征来吸收某些近红外线。
通过将红外线照在手指上，传感器可以感应到手指传输或反射的光，从而获得手指内部静脉的图像。关键在于通过红细胞
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