四叉树分割

Mean shift and other image segmentation algorithms.

说明：  极化SAR图像的去噪和分解，Lee滤波和精致Lee滤波，Freeman分解·和·H/A分解(Denoising and decomposition of polarimetric SAR image)

nsst域内基于区域能量和区域方差取最大值的图像融合算法，简单易懂。主程序高频区域能量取大，低频加权平均，读者可自行修改。(nsst maximum value within an image fusion algorithm based on regional energy and regional variance, easy to understand. Main high-frequency energy to take large area, low-weighted average, readers can modify.)

相关滤波技术讲解PPT，包含DSST详细过程的介绍(Correlation filter PPT , including the introduction of the DSST, MOSSE.)

实现了基于MATLAB的灰度图像缩小，可通过修改程序获得想要的性质(Reduction of gray image based on MATLAB.The desired nature can be obtained by modifying the program.)

基于分数阶微分的图像增强技术，代码可以根据图像的梯度信息，更好地增强图像的高频部分，非线性地保留低频部分。(Fractional differential enhancement technology-based image code according to the gradient information of the image can better enhance the high frequency part of the image, to retain the low-frequency part of the non-linear.)

gac模型的迎风方案可以实现对灰度图像的分割，分割效果还可以(gac)

非下采样contourlet相关去噪，论文程序，包括非下采样contourlet变换工具箱，已通过仿真(Related denoising nonsubsampled contourlet paper program, including non-sampling contourlet transform toolbox has been through the simulation)

混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)

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.)