sheyingceliang1

比较反滤波和维纳滤波对一幅图像复原的效果。从两方面出发，一是对运动模糊和均值模糊复原的能力，二是抗噪能力，各种情况分别都进行了对比(Comparison of inverse filtering and Wiener filtering on an image restoration results. Proceed in two ways, first on the motion blur and the mean fuzzy recovery capability and noise immunity, all cases are compared separately)

基于模型匹配的永磁同步电机混沌化控制例子，是本人一点点做出来的。(this is a example for Chaos Anti-Control of Permanent Magnet Synchronous Motor Based on Asymptotic Model Matching. it was completed by myself.)

这是二维图像双边滤波的相关程序，具有保边去噪的效果，适合对图像进行处理。(This is the two-dimensional image bilateral filtering procedures, with the edge-preserving denoising effect, suitable for image processing.)

说明：  计算两个离散变量x和y的联合熵z = H（x，y）。 输入：x，y：两个相同长度的整数向量输出：z：联合熵 可以使用，有利于学习目的(Compute joint entropy z=H(x,y) of two discrete variables x and y. Input: of x, y: two integer vector of the same length Output: z: joint entropy good for learning purposes)

这个代码是基于论文“Saliency Detection via Graph-Based Manifold Ranking”下的程序源码，作者是Chuan Yang, Lihe Zhang, Huchuan Lu, Ming-Hsuan Yang, and Xiang Ruan 功能1：使用canny算子计算图像的二值边缘图 (The code is for paper "Saliency Detection via Graph-Based Manifold Ranking" by Chuan Yang, Lihe Zhang, Huchuan Lu, Ming-Hsuan Yang, and Xiang Ruan Procedures: 1. compute a binary edge map of the image using the canny method. )

MRF代码实现,visual C++ 代码，不错，可以尝试(an implementation of MRF)

matlab小波分解信号的几个源程序，包括小波分解，时频转换一级功率谱分析(Matlab wavelet decomposition of the signal source program, including several wavelet decomposition, time-frequency transition level 1 power spectrum analysis)

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)

提出了基于小波变换的图像融合方法，并分析了可见光与红外图像的融合结果(An image fusion method based on wavelet transform is proposed, and the fusion results of visible and infrared images are analyzed.)

moravec算子提取特征点，然后用相关度进行匹配(moravec operator feature point extraction, and then match the correlation)