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Research on Enhancing Human Finger Vein Pattern Characteristics Based on Adjacent Node Threshold Image Method

机译:基于相邻节点阈值图像方法的人手指静脉纹特征增强研究

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An image of a finger captured under infrared light contains not only the vein pattern but also irregular shading produced by the various thicknesses of the finger bones and muscles. In this paper, we propose a new method to enhance the contrast of the finger vein image. It consists of five parts: wavelet denoising, normalization, adjacent node threshold image method, eliminate black block and burr, thinning. Firstly, we perform stationary wavelet decomposition and transform the image into four frequency bands and design different denoising methods to different frequency bands. Then, we nomalizate efficitive gray value range of finger-vein image to [0, 255]. And then, the binary image is got by adjacent node threshold image method. In the next, small blocks and burr are eliminated basing on the area size and by median filter algorithm separately. Finally, we obtain the skeleton by the quick thinning algorithm. The experiments show that the proposed method can properly enhance the contrast of the finger-vein image and make the skeleton express geometric structure of the hand vein image better.
机译:在红外光下捕获的手指图像不仅包含静脉图案,而且还包含由手指骨骼和肌肉的各种厚度产生的不规则阴影。在本文中,我们提出了一种新的方法来增强手指静脉图像的对比度。它由五个部分组成:小波去噪,归一化,相邻节点阈值图像方法,消除黑块和毛刺,细化。首先,我们进行平稳小波分解并将图像转换为四个频带,并针对不同的频带设计不同的去噪方法。然后,我们将手指静脉图像的有效灰度值范围标准化为[0,255]。然后,通过相邻节点阈值图像方法得到二值图像。接下来,根据面积大小和中值滤波算法分别消除小块和毛刺。最后,我们通过快速细化算法获得骨架。实验表明,该方法可以适当增强手指静脉图像的对比度,使骨架更好地表达手静脉图像的几何结构。

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