ALOHA-Matlab

二维地震信号的emd实现，逐道分解，挑选信号主导分量(EMD realization of two-dimensional seismic signal, decomposition by channel, selection of signal dominant component)

说明：  用于遥感图像分类。其输入为几幅遥感图像，使用k-mean聚类方法对图像中的不同地形进行聚类分割(For remote sensing image classification. Their input for a number of remote sensing images, the use of k-mean clustering method to image the topography of the different cluster partition)

三种图像分割法，有全局阈值otsu分割、迭代法全局阈值分割、局部阈值分割程序。(Three image segmentation method, there are global threshold otsu segmentation, iterative method global thresholding, local threshold segmentation program.)

一个简单的基于HOG的行人检测程序，MATLAB编写，其中read me文档解释的较为详细。(A simple HOG based pedestrian detection program, MATLAB write, read me the document which explains in some detail.)

何凯明暗原色去雾方法的matlab程序，按此种方法进行图像去雾(He Kaiming dark color to the fog method matlab program, according to this method the image to fog)

迭代阈值方法进行图象分割的函数,有测试程序和图片(threshold iterative method of image segmentation function, test procedures and Photo)

自己编写的数字图像处理程序，包括打开bmp图像，直方图，阈值分割，形态学处理等功能。(digital image processing procedures, including open bmp image, histogram, threshold segmentation, morphological processing and other functions.)

剪切波是小波变换的一种，常用于增强或者去噪，本代码用于去除图像噪声(The shear wave is a wavelet transform, often used to enhance or de-noising, the code is used to remove the image noise)

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).)

image decomposition and reconstruction