Diliver20120920yy

matlab函数，将多维矩阵的极值找出并定位，图像处理经常用到，很简单很好用(matlab function, multi-dimensional matrix to identify and locate the extreme, often used in image processing, it is very simple to use)

OTSU图像分割快速算法。原创，附参考文献。(a faster version of Otsu s method)

说明：  MATLAB如何将图片切分成任意M*N块 在图像处理的时候，有时会不可避免的将图像分块来进行操作，但是图像的像素不可能每次都是可以被整除的，所以我们需要imresize函数对图像的尺寸进行修改，以便可以完成M*N块的分割(How can matlab cut the image into any m * n blocks? Sometimes it is inevitable to divide the image into blocks for operation. However, the pixels of the image can not be divided every time. So we need to modify the size of the image with the function of reduce to complete the m * n block segmentation)

说明：  MATLAB 给图像添加各种噪声：如椒盐噪声，高斯噪声、乘性噪声等等。(MATLAB add all kinds of noise to the image: If salt and pepper noise, Gaussian noise, multiplicative noise and so on.)

sift算法在cv领域的重要性不言而喻，该作者的文章引用率在cv界是number1.本篇博客只是本人把sift算法知识点整理了下，以免忘记。本文比较早的一篇博文opencv源码解析之(3)：特征点检查前言1 中有使用opencv自带的sift做了个简单的实验，而这次主要是利用Rob Hess的sift源码来做实验，其实现在的opencv版本中带的sift算法也是Rob Hess的，只是稍微包装了下。 下面来做下试验,试验sift代码采用Rob Hess的代码，opencv目前版本中的sift源码也是采用Rob Hess的。代码可以在他的主页上下载：http://blogs.oregonstate.edu/hess/code/sift/ 这里我下载的是windows版本的，并采用Qt做了个简单的界面。(sift algorithm is self-evident the importance of the cv field, the author of the article referenced in the cv sector is number1 this blog I just sift algorithm knowledge compiled under, so as not to forget. This article compares the earlier blog post opencv source code analysis (3): check the preface of the feature points 1 the opencv bring their own sift to do a simple experiment, but this time Rob Hess' s sift source to experiment. In fact, with the opencv version sift algorithm is Rob Hess, only slightly packing the next. Below to do the next test, test sift code using the code of Rob Hess, sift source opencv version of Rob Hess. The code can be downloaded on his home page: http://blogs.oregonstate.edu/hess/code/sift/ here I downloaded the windows version, and using Qt to do a simple interface.)

说明：  在一图像中加入噪声，是超分辨率中的一个重要步骤哦(noise)

说明：  通过选取初始种子点，进行图像的阈值分割，通过对比计算该点8领域灰度值是否符合阈值条件来对图像边缘进行分割(The image edge is segmented by choosing the initial seed point and comparing the gray value of the 8 domains of the point with the threshold condition.)

traitement d images contours et formes

说明：  数字图像处理的大作业，有报告，关键代码都有注释，有一些不足，可供大家参考(Digital image processing operations, there are reports, key codes have notes, there are some shortcomings, for your reference.)

Retinex理论的基础理论是物体的颜色是由物体对长波（红色）、中波（绿色）、短波（蓝色）光线的反射能力来决定的，而不是由反射光强度的绝对值来决定的，物体的色彩不受光照非均匀性的影响，具有一致性，即retinex是以色感一致性（颜色恒常性）为基础的。不同于传统的线性、非线性的只能增强图像某一类特征的方法，Retinex可以在动态范围压缩、边缘增强和颜色恒常三个方面打到平衡，因此可以对各种不同类型的图像进行自适应的增强。(The basic theory of Retinex theory is the color of the object by object for the long wavelength (red), medium (green), short wave (blue) light reflection ability to decide, rather than by the intensity of reflected light of the absolute value to decide, the color of the object under illumination nonuniformity effects, consistent with the Retinex, that is based on color constancy (color constancy based). Different from the traditional linear, nonlinear image enhancement method of only one kind of features, Retinex can be in dynamic range compression, edge enhancement and three aspects of color constancy hit the balance, thus enhancing adaptive to a variety of different types of images can be.)