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小波包变换去噪和滤波去噪的word，可以直接剪切到MATLAB。有分析(Wavelet Packet Denoising and filtering denoising word, you can cut directly to MATLAB. Some analysts)

HOG特征提取源代码，这份代码是针对行人检测编写的，可以根据个人需要设置参数。(HOG Feature Extraction source code, this code is written for pedestrian detection, you can set the parameters according to individual needs.)

一种像素级指纹方向场估计算法以及对应的像素级奇异点检测算法。(A pixel-level orentation field estimation algorithm and the corresponding singular point detection method.)

高斯核支持向量数据描述（SVDD）方法的源代码 (the code of gauss kernel support vector data description)

文简要介绍了指纹识别的基本步骤，分别是指纹图像预处理、指纹特征提取、指纹匹配(This paper briefly introduces the basic steps of fingerprint recognition, which are fingerprint image preprocessing, fingerprint feature extraction and fingerprint matching)

对整幅三维影像数据进行Butterworth高通滤波处理（10阶2次），先进行傅里叶变换，利用巴特沃斯高通滤波器数学模型进行变换，在傅里叶逆变换得到新数据(For the entire three-dimensional image data Butterworth high-pass filtering (10-order twice), the first Fourier transform, use Butterworth high-pass filter to transform the mathematical model, the inverse Fourier transform to obtain new data)

主成分分析程序包，包括主成分分析和独立主成分分析两个程序源代码。(Principal component analysis package, including principal component analysis principal component analysis and independent source code for both procedures.)

SAR图像的去噪，包含lee滤波器，frost滤波器，kuan滤波器，并且自己编写的信噪比函数(SAR image denoising, including lee filter, frost filter, kuan filter, and I have written a function of SNR)

说明：  全变分去噪程序，用于图像去噪，该方法能克服图像去噪中的震荡效应，使图像更加光滑，并能保存边缘信息。(total variation denoisy)

视网膜血管检测的Gabor变换和机器学习，教程 本教程将演示如何Gabor变换和广义 的线性模型（GLM）可用于视网膜血管检测 图像。 ，我们将尝试检测视网膜血管从 的训练图像，首先，Gabor滤波器与图像卷积。 GLM将使用Gabor变换的图像特征确定 （独立变量）和容器的位置 为结果（因变量）。( Retinal Vessel Detection by Gabor Transform and Machine Learning, a Tutorial This tutorial will demonstrate how Gabor transforms and generalized linear model (GLM) can be used for detection of retinal vessels in images. Specifically, we will attempt to detect the retinal vessels a training image , by first, convoluting multiple Gabor filters with the image. A GLM will be determined using the Gabor transformed images as features (the independent variables), and the locations of the vessels as the outcome (the dependent variable).)