ImageEdgeProcessing

关于奇异值分解，图像特征融合，图像处理的特征图提取方法(About the singular value decomposition )

基于非下采样轮廓波变换的图像融合。低频分量取平均，高频分量取绝对值最大。(nonsubsampled contourlet transform.Averaged low frequency components, high frequency components of the maximum absolute value.)

本ACIS几何造型平台组件提供基本的拓扑构造，线框构造和编辑，性质分析（面积长度和质量等）等功能。(the ACIS geometric modeling platform components provide basic topological structure, wireframe construction and editing, Analysis (area of length and quality, etc.) and other functions.)

说明：  给图像加扰动，扰动方式以zernike多项式的形式加入，Image为源图像，Co为要加的zernike系数，为行向量， 输出分别为扰动后的图像，扰动PV值，扰动RMS值(failed to translate)

FCM和稀疏场水平集用于分割医学图像分割(FCM and sparse field level set for segmentation of medical image segmentation)

用以图像分类，可以设计自己的数据集训练模型(For image classification, you can design your own data set training model)

编程实现二维8*8图像块的DFT，DCT变换 输入一副RGB图像，将其转化为YCbCr颜色空间，然后对Y分量做分块DFT变换，保存为灰度图像，统计计算时间 编程实现8*8块DCT变换,然后分别显示，当使用64个DCT系数里面的一些系数进行重构，比较其质量；同时熟悉采用FFT来实现DCT变换的思想，并显示2维8*8DCT变换的基函数图像(Programming the DFT, DCT transform of two dimensional 8*8 image block Input a pair of RGB images, transform it into YCbCr color space, then divide the Y component into block DFT transform, save as gray image, and calculate the calculation time The 8*8 block DCT transform is implemented by programming, and then it is shown that when using some coefficients in 64 DCT coefficients, the quality is reconstructed. Meanwhile, the idea of DCT transformation is familiar with FFT, and the base function image of the 2 dimensional 8*8DCT transform is displayed.)

彩色图像的梯度边缘提取算法matlab程序(matlab edge detection)

缺陷检测，顶帽变换，底帽变换，halcon，可以直接使用(Defect detection, top hat transformation, and bottom cap transform, Halcon, can be used directly)

本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。 从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )