Zemax+DDE+toolbox+for+Matlab

基于K均值聚类的图像分割，灰度图像分割案例，真彩图像分割案例(K-means clustering-based image segmentation, image segmentation case, the true color image segmentation case)

二值图像连通域分析，和MATLAB中bwlabel函数功能相似(Connected domain analysis of binary image, and bwlabel similar function in MATLAB)

基于光流法的二维单模态图像配准，能得到变形场(This will allow you to generate the deformation field needed for contour guided multi-modal image registration.)

主要完成图像特征出提取，包括5个特征选择算法：SFS，SBS，SFBS(Description The DEMO includes 5 feature selection algorithms: Sequential Forward Selection (SFS) Sequential Floating Forward Selection (SFFS) Sequential Backward Selection (SBS) Sequential Floating Backward Selection (SFBS) ReliefF)

对图像进行旋转和缩放的源代码,支持原图像的抽样处理及任意角度的旋转(The image rotation and scaling in any direction)

PAMI2015 基于核的相关滤波跟踪方法 可以在32位机上运行 因为包含gradientMex.mex32文件( Kernelized Correlation Filter PAMI2015 the code can run on 32-bit computer because it contains file gradientMex.mex32.)

说明：  经典的粒子滤波算法，适用于粒子滤波的初学者更好的了解粒子滤波(Classical particle filter, particle filter for better understanding of particle filter for beginners)

一个基于脉冲耦合神经网络的高斯噪声滤波程序，包括源码和实验结果(pulse coupling based on a neural network Gaussian noise filtering procedures, including source and experimental results)

Continuous Birdsong Recognition Using Gaussian Mixture Modeling of Image Shape Features

转：https://blog.csdn.net/jinshengtao/article/details/26278725 混合高斯背景建模是基于像素样本统计信息的背景表示方法，利用像素在较长时间内大量样本值的概率密度等统计信息(如模式数量、每个模式的均值和标准差)表示背景，然后使用统计差分(如3σ原则)进行目标像素判断，可以对复杂动态背景进行建模，计算量较大。 在混合高斯背景模型中，认为像素之间的颜色信息互不相关，对各像素点的处理都是相互独立的。对于视频图像中的每一个像素点，其值在序列图像中的变化可看作是不断产生像素值的随机过程，即用高斯分布来描述每个像素点的颜色呈现规律单模态(单峰)，多模态(多峰)(Gaussian mixture background modeling is a background representation method based on the statistical information of pixel samples. Statistical information such as the number of patterns, the mean and standard deviation of each pattern are used to represent the background. Statistical difference (such as 3_principle) is used to judge the target pixel. Complex dynamic background modeling has a large amount of computation. In the Gaussian mixture background model, it is considered that the color information between pixels is uncorrelated and the processing of each pixel is independent of each other. For each pixel in a video image, the change of its value in a sequential image can be seen as a random process that produces pixel values continuously, i.e. Gaussian distribution is used to describe the regularity of color rendering of each pixel in single mode (single peak) and multi-mode (multi-peak).)