JDImgRect

基于meanshift算法的目标跟踪，主要是基于opencv车辆跟踪！！！程序当中进行简单的图像分割(Based on the meanshift algorithm of image segmentation and target tracking.)

用IDl编写的，用IDL图像处理。已调试好的程序。(Prepared using IDL with IDL image processing. Debugging process has been good.)

图像的高斯与拉普拉斯金字塔分解，两种方法都有(Image gaussian and Laplacian pyramid decomposition, there has two kinds of method)

基于样本的纹理合成，采取点替换的方法，可以合成brodatz类的纹理。采取的方法是先对图像分析，然后合成。(Texture synthesis based on samples taken at the replacement of the method of texture synthesis brodatz category. Approach taken is to firstly image analysis, and then synthesis.)

这是我编写的数学形态学处理灰度图像和二值图像的源代码，没有使用matlab自带的函数。可以实现膨胀、腐蚀以及开闭运算。(This is written in mathematical morphology of grayscale images and the source code of the binary image, do not use Matlab comes with function. Can achieve expansion, corrosion, and the opening and closing operation.)

基于IHS变换的图像融合matlab程序，基于IHS变换和小波帧变换的图像融合matlab程序。(IHS transform based image fusion matlab program, based on the IHS transform and wavelet frame transform matlab image fusion procedures.)

现对图像进行边缘提取，然后利用霍夫变换提取图像中的圆环(Is image edge extraction, and then extracted using Hough transform image circle)

功能很全的车牌识别系统，图像与处理，车牌定位，字符分割和字符识别等(Full license plate recognition system, image processing, license plate location, character segmentation and character recognition.)

稀疏矩阵字典的优化KSVD，本人已进行测试，效果甚佳,可以用在图像，数据的压缩和分类(Sparse dictionary optimization KSVD, I have been tested, very good results)

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. )