exp_2_1

基于相位相关的图像配准，图像的移动参数代码(Phase correlation-based image registration, image of the movement parameter code)

说明：  利用经典免疫遗传算法实现单阈值图像的分割。(Classical immune genetic algorithm is used to segment single threshold image.)

小波去噪的程序，很好用，推荐给大家使用。(Wavelet denoising procedures, very easy to use, recommended for everyone to use)

结构光重建MATLAB模拟代码，采用PMP原理(MATLAB simulation code for structural light reconstruction)

水表指针识别系统，能够从外部导入任何一个bmp图像，最好是水表图，能够对该图形操作并读出图中水表的度数 (meter target identification system from the outside into any one bmp image, preferably meter map, Graphics to be able to operate and read out the map meter reading)

说明：  左右圆偏振光的推导公式，与处理结果。传统意义上根据界面曲率的不同，对于确定的透镜，其正极性或负极性是确定了的。但根据等离子体天线方向角的改变，透镜的部分极性也随之改变，换言之，同一透镜中正负极性均存在且可控制。(Derivation formula and processing results of left and right circularly polarized light)

说明：  铁轨线检测，可以完成对直轨和弯轨的检测，效果较好(Rail line detection, can complete the detection of straight rail and curved rail, the effect is good)

利用Orb算法实现图像特征点的提取，并且通过前后两帧之间的特征点进行特征匹配，为视频稳像、图像融合以及图像识别提供前提条件。(Detecting feature points using Orb algorithm, then matching the points with the detected points frames.That method provide one new way to video stablization ,image fusion and image recognition etc. )

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)

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.)