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小波包程序2，能够对信号进行特征提取，有利于初学者。(This is a wavelet packet program 2，which is useful for the beginners.)

基于格子玻尔兹曼方法模拟泊肃叶流，边界条件用非平衡格式(Poiseuille flow based on the lattice Boltzmann method to simulate the boundary conditions with unbalanced format )

图像处理 二值化 旋转 90 180 等，支持多种 图片(图像处理 二值化 旋转)

说明：  opencv中的一段 snake的代码, opencv snake,(A piece of Snake code in opencv, opencv snake,)

说明：  使用目标检测的基本常用算法的帧间差分法来对视频中的运动目标进行检测(moving object detection)

matlab图像处理代码，利用分裂、合并算法进行图像的分割，含m文件和GUI(matlab image processing code, using split, merge image segmentation algorithms, including documentation and GUI m)

直方图均衡化，包括动态直方图均衡化，全局直方图均衡化，局部直方图均衡化，以及绘制图像的直方图 其中动态直方图均衡化用A dynamic histogram equalization for image contrast enhancement 只取x=0情况(Histogram equalization, including dynamic histogram equalization, global histogram equalization, local histogram equalization, and where to draw the image histogram dynamic histogram equalization with A dynamic histogram equalization for image contrast enhancement only take x = 0 Happening)

先进行图像预处理，比如Hough变换，边沿检测，还有差影法，模板提取 ，识别，跟踪，最后是模板匹配。(First image preprocessing, such as the Hough transform, edge detection, as well as poor shadow method, template extraction, identification, tracking, and finally the template matching.)

在图像中加入椒盐噪声的源代码，编译后的可执行文件须带参数运行(the image Salt and Pepper to the noise source, the compiled executable file parameters required to bring Operation)

（1）计算图像中每个像素点的LBP模式(等价模式,或者旋转不变+等价模式)。 （2）然后计算每个cell的LBP特征值直方图，然后对该直方图进行归一化处理(每个cell中，对于每个bin,h[i]/=sum,sum就是一副图像中所有等价类的个数)。 （3）最后将得到的每个cell的统计直方图进行连接成为一个特征向量，也就是整幅图的LBP纹理特征向量； 然后便可利用SVM或者其他机器学习算法进行分类识别了。((1) calculate the LBP pattern of each pixel in the image (equivalent mode, or rotation invariant + equivalent mode). (2) then the LBP eigenvalue histogram of each cell is calculated, and then the histogram is normalized (for each cell, for each bin, h[i]/=sum, sum is the number of all the equivalent classes in a pair of images). (3) finally, the statistical histogram of each cell is connected into a feature vector, that is, the LBP texture feature vector of the whole picture. Then, SVM or other machine learning algorithms can be used for classification and recognition.)