Haar_2D

应用小波变换 模极大值算法实现医学图像（CT图像和MRI图像）的融合(Wavelet transform modulus maxima to achieve medical image fusion)

把图像导入后，可以实现对图像的huffman编码(Import the images, you can realize the image of the huffman encoding)

去除彩色图像中的阴影，以便于图像分割，物体识别以及跟踪(To remove the shadow of color images in order to facilitate the image segmentation, object recognition and tracking)

此程序是基于互相关的亚像素级图像匹配的代码，此种配准方法误差小，精度高。(This procedure is based on the cross-correlation of sub-pixel image matching of the code, such a registration method error and high accuracy.)

opengl 导入obj以及纹理，并且读入mtl啦啦啦啦绿撒的发生大(opengl objfsdagfadgadsfgsa)

在MATLAB平台下，可以实现散斑的生成。(The generation of speckle is realized under the MATLAB platform.)

说明：  均值方差与协方差 协方差矩阵、特征值与特征向量的提取、PCA原理 人脸识别算法之EigenFace、LBPH 人脸识别应用开发案例(Mean Variance and Covariance Covariance Matrix, Extraction of Eigenvalues and Eigenvectors, PCA Principle EigenFace and LBPH of Face Recognition Algorithms Case Study of Face Recognition Application Development)

PCB板检测的大概流程如下：首先存储一个标准PCB板图像作为参考标准，然后将待检测的PCB板图像进行处理，比较与标准PCB图像的差异。根据差异的情况来判断缺陷类型。(The approximate process of PCB board detection is as follows: first, a standard PCB board image is stored as a reference standard, and then the PCB board image to be detected is processed to compare the difference between the standard PCB image and the standard image. The type of defect is judged according to the difference.)

图片的加法运算减法运算乘法运算除法运算和四则运算(Four Operations of Addition, Subtraction, Multiplication and Multiplication of Pictures)

说明：  机器学习专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技 能，为包括人脸识别领域的人工智能的发展做出了极大的贡献。本文探索如何应用机器学习中的一些技术，使计算机更好地完成人脸识别领域中的人脸检测和人脸验证。 在人脸检测方面，针对如何快速、准确地检出人脸的问题，基于DLIB中特征模型，可以快速提取检测出人脸，并且提取出人脸特征点,主要作用就是快速检测定位人脸。在训练过程中，引入了ResNet机器学习算法，该算法采用多层卷积神经网络结构，对人脸进行多层特征提取和描述，得到人脸特征描述符。通过测试可以证实了上述方法能够检测和识别人脸。(In terms of face detection, for the problem of how to detect faces quickly and accurately, based on the feature model in DLIB, you can quickly extract and detect faces, and extract face feature points, the main role is to quickly detect and locate faces. In the training process, the VGG-FACE machine learning algorithm is introduced. This algorithm uses a multi-layer convolutional neural network structure to perform multi-layer feature extraction and description on the face to obtain the face feature descriptor. Tests can confirm that the above method can detect and recognize human faces.)