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基于Gabor小波变换的特征提取和人工智能的人脸检测系统源代码.(Based on the Gabor wavelet feature extraction and artificial intelligence face detection system source code.)

三维时域有限差分法结合UPML吸收边界条件模拟程序(Three-dimensional finite-difference time-domain method combined with UPML absorbing boundary conditions)

简单的帧间差分法，不过可以和其他方法结合来改进算法(A simple inter-frame difference method, but other methods can be combined to improve the algorithm for)

非常详细的一篇论文，仔细介绍了手指静脉识别的的全过程(A very detailed paper, carefully introduced the finger vein identification process)

使用matlab实现的,基础单变量以及多变量梯度下降算法,和信号分类以及信号归一化部分算法，是机器学习领域最基本的代码，希望大家多多交流(matlab implementation of basic univariate and multivariate gradient descent and signal classification algorithms, machine learning basic code, hope that more exchanges)

算法实现消除小面积的区域,保留面积大的区域.对去除小斑点很稳健.(Algorithm to achieve the elimination of small areas of the region to retain the large area of the region. For the removal of small spots very sound.)

基于自适应的区域选择和自适应的正则化得图像去模糊和超分辨处理，试过可以用(Based adaptive region selection and adaptive regularization was image deblurring and super-resolution processing)

DIC测试，适用于二维数字图像相关法，采用的ZNSSD，输入图像需为灰度图像，输出仿射矩阵(Measuring strain in samples which are too small, big, compliant, soft or hot are typical scenarios where non-contact techniques are needed. A technique which can cover all that and also can deal with complicated strain fields in structures or structural materials is the Digital Image Correlation. With this technique, strain can be calculated from a series of consecutive images with sub pixel resolution as will be shown in the following chapters.)

量化离散余弦变换系数的程序实现及其效果的检测。过程中使用了8x8的分块方法。首先计算每块的离散余弦变换（DCT)；然后量化了DCT的系数； 最后进行了反离散余弦变换(iDCT). 程序中还可以设定缩放系数。(Quantify the discrete cosine transform coefficients procedure and its effect detection. The process used in the 8x8 sub-block method. First calculate the block discrete cosine transform (DCT), quantization of the DCT coefficient Finally, the inverse discrete cosine transform (iDCT) program can also set the scaling factor.)

由于彩色图像提供了比灰度图像更为丰富的信息, 因此彩色图像处理正受到人们越来越多的关注。彩色图像分割是彩色图像处理的重要问题, 彩色图像分割可以看成是灰度图像分割技术在各种颜色空间上的应用, 为了使该领域的研究人员对当前各种彩色图像分割方法有较全面的了解, 因此对各种彩色图像分割方法进行了系统论述, 即先对各种颜色空间进行简单介绍, 然后对直方图阈值法、特征空间聚类、基于区域的方法、边缘检测、模糊方法、神经元网络、基于物理模型方法等主要的彩色图像分割技术进行综述, 并比较了它们的优缺点, 通过比较发现模糊技术由于能很好地表达和处理不确定性问题, 因此在彩色图像分割领域会有更广阔的应用前景。(colorful image segment)