ruanyuzhiquzao

实现单尺度retinex算法，用于图像的对比度增强，对低能见度图像处理效果较好。(Retinex single-scale algorithm for image contrast enhancement, image processing on low visibility is better.)

meanshift 分割算法meanShift,均值漂移，在聚类、图像平滑、分割、跟踪等方面有着广泛的应用。meanShift这个概念最早是由Fukunage在1975年提出的，其最初的含义正如其名：偏移的均值向量；但随着理论的发展，meanShift的含义已经发生了很多变化。如今，我们说的meanShift算法，一般是指一个迭代的步骤，即先算出当前点的偏移均值，然后以此为新的起始点，继续移动，直到满足一定的结束条件。(meanshift Segmentation algorithm)

说明：  本程序是基于小波和盒维数的车型识别程序，可以选择不同车型，有GUI界面，有图片，有程序，能运行，相信初学者一定能够获益。(This program is based on the wavelet and box dimension of the vehicle type recognition program, you can choose different models, GUI interface, pictures, programs, can run, I believe beginners will benefit.)

介绍了不同非线性模型在随机共振中的应用，像饱和非线性，阈值非线性等(Describes the different nonlinear model of stochastic resonance in the application, such as nonlinear saturation threshold nonlinearity. . . .)

说明：  使用matlab实现图像的JPEG压缩算法的实现(Realization of JPEG compression algorithm of image with MATLAB)

hough变换对圆的检测源代码，附带图片以及程序说明(hough transform the detection of circular source code, fringe shows and procedures Photo)

功能简单的截图，可以像qq那样截图，并有图片处理功能(Simple screenshots, like QQ screenshots, and image processing capabilities)

目前MATLAB里边对于多点计算仿射矩阵普遍采用RANSAC法，但是比较麻烦计算量也相对较大，通过最小二乘法来进行仿射矩阵的估计能够大大提高该效率，所以奉上自己在实验室写的仿射矩阵估计函数，来弥补matlab函数库在这方面的不足。(The MATLAB inside for calculating affine matrix commonly used RANSAC method, but more trouble calculation is relatively large, estimated by the least squares method to the affine matrix can greatly improve the efficiency, so give yourself in laboratory to write the estimated affine matrix function, to compensate for the lack of MATLAB Library in this area.)

六种图像增强的方法，线形反锐化掩膜方法，直方图均衡等。(6 kinds of image enhancement methods, linear unsharp mask method, such as histogram equalization.)

最小二乘法拟合圆vc实现，最小二乘法(least squares analysis)是一种 数学 优化 技术，它通过 最小化 误差 的平方和找到一组数据的最佳 函数 匹配。 最小二乘法是用最简的方法求得一些绝对不可知的真值，而令误差平方之和为最小。(least squares fitting round vc realized, least-squares method (least squares analysis) is a mathematical optimization techniques, It error by minimizing the square and a group of data to find the best matching function. Least-squares method is the most simple method to derive absolutely true value unknown, and so the sum of squared errors to the minimum.)