cat-swarm-optimizaiton_

用于脸部识别时，使用基于像素和区域的图像特征核的稀疏表示的识别算法。(When used in face recognition using image recognition algorithm based on feature pixel and regional nuclear sparse representation.)

atrous NSCT图像融合算法 可直接使用 带有实验测试图(atrous NSCT image fusion )

基于MATLAB的汽车车牌牌照的识别。利用BP神经网络做GUI界面(Recognition of Vehicle License Plate License Based on. Using BP neural network to do GUI interface)

在边缘检测之后得到的边缘不一定是完整的，利用这个函数可以将个线段生长一些，然后连接起来，成完整的形状(At the edge of the edge detection after get is not necessarily complete, use this function can be a line segment to grow some, and then connect, into the shape of a complete)

关于遥感影像分类和变化检测的一本外文数据，其特色在于用IDL实现了很多经典算法，很值得借鉴！(Foreign language data on a remote sensing image classification and change detection, which feature IDL achieve many classical algorithm, it is worth learning!)

双目立体视觉，采用两张图像恢复三维形状，外国人的一个开源项目。(System Prototype to make 3D reconstruction solution using stereo images. It works with common cameras and not require large amount of memory during the images processing. It provides a low cost solution to educational environments with low budgets. )

经典的4种彩色图像分割方法：CSC.EGBIS.JSEG.Meanshift 的VC++实现；及相关英文文献。绝对值得拥有！哈哈...(The classic four kinds of color image segmentation method: CSC.EGBIS.JSEG.Meanshift of VC++ implementation and the relevant literature in English. Definitely worth having!ha ha ...)

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. )

说明：  海森矩阵在图像处理中有许多应用，此代码为海森矩阵的matlab代码(The Hessian matrix has many applications in image processing. This code is the matlab code of the Hessian matrix.)

使用傅立叶变换对图像的条纹噪声进行滤除，分别使用了理想低通滤波、巴特沃兹低通滤波和高斯低通滤波进行处理(The use of Fourier transform on the image to filter out the noise of the stripes, respectively, using an ideal low-pass filtering,巴特沃兹Gaussian low-pass filtering and low pass filter to deal with)