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图像背景提取，Matlab写的，效果还不错。(Image background extraction)

生成拉盖尔函数、缔合拉盖尔函数，并根据这两个函数的解析表达式画出若干个拉盖尔-高斯光束模型(Generation of Laguerre functions, associative Laguerre functions, and according to the analytical expression of these two functions to draw a number of Laguerre- Gaussian beam model)

说明：  RM的编码过加性高斯白噪声信道（AWGN）后译码的C++ 实现程序，C语言产生高斯白噪声(RM of coded additive white Gaussian noise channel (AWGN) after decoding in C++ program, C language generated Gaussian white noise)

维纳滤波法去除加有高斯噪声、椒盐噪声、乘性噪声(Wiener filter method to remove the addition of Gaussian noise, salt and pepper noise, multiplicative noise)

本代码是基于数学形态学的多尺度多结构算子的腐蚀与膨胀算法(This code is corrosion and expansion algorithm based on mathematical morphology of multi-scale multi-structure operator)

基于PCA主成分分析算法和Yale人脸数据库，采用Matlab语言实现的人脸识别程序，整个程序分为预处理、训练、识别、GUI界面控制等几个模块，注释清晰，通俗易懂。(Based on PCA principal component analysis algorithm and Yale face database, the face recognition program is implemented in Matlab language. The whole program is divided into several modules, including pretreatment, training, recognition, GUI interface control and so on. The annotation is clear and easy to understand.)

在qt5上实现图像处理界面的设计，实现基本的图像处理算法，结合opencv(Implement image processing and interface design on qt5, to achieve the basic image processing algorithms, combined with opencv)

一个改进的卡尔曼滤波器 用来进行运动目标的跟踪 (An improved Kalman filter used for tracking moving targets)

本代码建立高斯混合模型(高斯多模型)(GMM)，将其用于计算机视觉领域的视频目标检测/视频监控/运动检测/运动目标检测/视频目标跟踪等相关应用中。(This code sets up Gaussian mixture model(Gaussion Mixture Model, GMM) with image sequences for the research and application field of computer vision, using GMM for visual object detection(video object detection), visual surveillance, motion detection, motion tracking, visual object tracking, video object tracking, etc.)

实现简单图像处理，包括256色转灰度图、Hough变换、Walsh变换、中值滤波、二值化变换、亮度增减、傅立叶变换、反色、取对数、取指数、图像平移、图像旋转、图像细化、图像缩放、图像镜像、均值滤波、对比度拉伸、拉普拉斯锐化（边缘检测）、方块编码、梯度锐化、灰度均衡、直方图均衡、离散余弦变换、维纳滤波处理、逆滤波处理、阈值变换、高斯平滑。(simple image processing, including color to grayscale 256, the Hough transform, Walsh transform, Median filtering, binary conversion, brightness changes, Fourier transform, and color-from the few to admit index, image shifting, image rotation, refined images, image scaling, image mirroring, mean filtering, contrast stretching, Laplacian sharpening (edge detection), block codes, Sharpening gradient, gray balance, histogram equalization, discrete cosine transform, Wiener filter, reverse filtering, threshold transform, Gaussian smoothing.)