14、字典学习

hough变换是图像处理中一种方法，该文件是用matlab语言实现的hough变换，但未使用matlab提供的函数，可以用于hough变换的学习和掌握。(hough ??换 是??像 )

图像处理中经常要用到的提取方向特征的gabor滤波程序(gabor filter map)

说明：  本程序使用MATLAB编程用于计算图片的视觉混乱度(Calculate the visual clutter of pictures)

说明：  QR二维码识别中在对图像预处理后最重要的一步就是要进行定位，QR二维码中有三个位置探测图形，通过扫描其特征便可以进行精细定位，提出了一种新的算法用于筛选候选点。(In QR code recognition, the most important step after image preprocessing is to locate. There are three position detection patterns in QR code, which can be precisely located by scanning its features. A new algorithm is proposed to screen candidate points.)

阴影消除matlab代码，运行可用，可供参考(The shadow eliminates the matlab code, which is available for reference)

应用kmean均值聚类方法对RGB,HSR图像的分割算法，个人觉得自己变得还可以，希望大家借鉴。(application kmean means clustering method of RGB, neighborhood image segmentation algorithm, individuals feel that they can become the hope that you learn from.)

SRCNN Superresolution imteplated by tensorflow SRCNN tensorflow 实现(SRCNN Superresolution imteplated by tensorflow)

说明：  自动检测干涉条纹中心线算法，代码功能强大，模块很多，方便逐一了解，全自动的算法优势。(Automatically detects interference fringes centerline algorithm, code, powerful, a lot of modules, easy to understand one by one, fully automatic algorithm advantage.)

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. )

图像纹理特征提取，是图像处理中一另一个非常重要的底层特征，matlab实现(Image texture feature extraction, image processing are very important to one another underlying characteristics, matlab implementation)