matlab 处理

摄像头调用，并用摄像头拍照，再对照片进行处理(matlab Camera call)

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：  区域生长算法。不用手动选择种子点，可自动选择。(Image segmentation by growing region method. No manual selection of seed points, automatic selection)

MSE，最小均方误差,用于图像匹配，可用，亲测(MSE, minimum mean square error for image matching, available, pro-test)

在google earth engine云平台对影像进行自动分类提取，需要手动输入样本及研究区(Automatic image classification and extraction on Google Earth engine cloud platform requires manual input of samples and research areas)

基于OpenCV的MPEG-7特征提取，c++代码(MPEG-7 feature extraction based on OpenCV)

用单阈值三种方法实现肺野分割的Matlab程序(A single threshold value of three methods of achieving lung field segmentation of the Matlab program)

BRISK特征点提取源代码，Linux及Windows，MATLAB环境。(Linux and Windows)

自己编写的二维傅里叶变换的源代码，并在变换后显示频谱。(Two-dimensional Fourier transform of the source code I have written, and displayed in the transformed spectrum.)

说明：  转载的，通过眨眼前后灰度图对比是别人眼，一个学习opencv工具的好例程。(Reproduced by blinking eyes of others around the grayscale contrast, a learning tool opencv good routine.)