my-DPCM

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)

softcast的2d dct实现，先对图像转灰度图，得到的亮度信号矩阵做DCT 变换(softcast of 2d dct implementation, the first image to grayscale, luminance signal obtained by DCT transformation matrix do)

应用DCT字典和KSVD学习字典，对图像进行简单的修复，可行性较高，速度不是很快。(Simple image restoration is feasible and not very fast.)

一些matlab图像图像处理例程,骨架,灰度图像梯度,凸壳,细化.(Some image image processing matlab routines, skeleton, grayscale gradient, convex hull, thinning.)

自行编写的逆投影变换，可实现图像坐标系到世界坐标系的转换，从实现鸟瞰变换。(the IPM ,which is completed by me, is able to achieve the transformation between the image coordinates and world coordinates )

K-meansK均值聚类在无监督的情况下选择图像特征的算法(K-meansK means clustering in the case of unsupervised image feature selection algorithm)

ostu算法（最大类间房差法，类属于阈值分割）的maltab代码，用于计算机图像处理分割及其相关应用。(ostu algorithm (the largest difference room category, type of threshold segmentation is) the maltab code, used in computer image processing and related partition application.)

包含实验图片，实验代码,对于从事数字图像处理工作有一定帮助(including experimental images and agrithms.It is benificial to image procecing workers.)

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：  利用matlab进行插值法亚像素边缘检测，提取零件轮廓(Subpixel edge contour extraction)