第五次作业

单目相机标定，非常好用，可在visual studio以及visual C++上运行(calibration for camera)

数字图像处理中sobel算子的手写代码；canny手写代码；加噪声并滤波的调用库函数代码(Digital image using sobel & canny)

MATLAB编写的增强图像分辨率的程序，可以提高图像分辨率，使图像更加清晰。(Written MATLAB program enhanced image resolution, image resolution can be improved to make the image clearer.)

数字散斑相关程序，能够计算2副图片的位移，简单的例子，对初学者也许有用。(Digital speckle correlation program can calculate the displacement of two images, a simple example, may be useful for beginners.)

此程序利用openCV中人脸识别于模版匹配实现机器人的运动控制功能，其中机器人控制用了Aria的库和模拟器，使用者可根据控制需要替换这部分程序。 本程序的核心部分一直用于只能轮椅的人脸控制。(This process us face recognition in openCV and template matching to achieve robot motion control functions, which used the Aria library and robot simulator, user need replace this part of the control procedures to achieve their application.)

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.)

BMP转位图 6410 logo bmp-C(BMP bitmap transfer)

双目立体匹配，使用SAD，SSD，NCC算法，比较多种常见算法的优劣，对其优化实现快速准确匹配。(Binocular stereo matching, the use of SAD, SSD, NCC algorithm, compare the merits of a variety of common algorithms, and its optimization to achieve fast and accurate matching)

Shap Context based Shape recognition

超像素分割程序，既有原码，也有可执行程序，验证过，好用。(Super-pixel segmentation procedure, both the original code, there are executable programs, proven, easy to use.)