pingjiehefengbu

demons 配准非常好用的非刚体配准！(Registration is very easy demons non-rigid registration!)

最小二乘图像影像匹配方法，一个很详细的源码例程，有过程有结果，希望对初学者有帮助。高斯解方程，以及最小二乘匹配，里面都很详细。(Squares image image matching method, a very detailed source code routines, the outcome of the process, I hope to help beginners. Gaussian solution of equations, and least squares matching, which is very detailed.)

horn天线的仿真模型及结果 可以进行进一步的 实验设计 以及应用到其他场景的喇叭模型(Simulation model and results of horn antenna)

这是用C++实现图像立体匹配的一个完整的论文代码，主要特性是提出了一种全新的跨规模代价聚合方法，适合对于图像的立体匹配有一定基础的人学习(Cross-Scale Cost Aggregation for Stereo Matching)

椭圆、圆、矩形等的识别，在MATLAB下运行.(Ellipse, round, rectangle recognition.)

说明：  该算法利用概率论中最大似然估计实现EM算法，通过对理论图像和统计图像的比较得出结果。(The algorithm uses the probability of the maximum likelihood estimate EM algorithm, the results of the comparison of theoretical imagery and statistical image.)

说明：  使用python语言编写卷积神经网络的卷积结构。(The convolution structure of convolution neural network is written in Python language.)

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)

图像处理作业：基于高频补偿算法实现图像滤波及分析 数值分析作业：四种方法实现求解根号3的值(Image processing operations: image filtering and analysis of numerical analysis operations: four methods based on high-frequency compensation algorithm to solve the root of the value)

说明：  次文件夹包含两个程序，可以对振动信号进行包络分析，适合信号处理入门的朋友(do envelope analysis on vibration signal)