ostu

MRF代码实现,visual C++ 代码，不错，可以尝试(an implementation of MRF)

二值图像处理运算是从数学形态学下的集合论方法发展起来的,尽管它的基本运算很简单，但是却可以产生复杂的效果。常用的二值图像处理操作有许多方法，如腐蚀、膨胀、细化、开运算和闭运算等等。本文对这些内容作些研究探讨， 希望对爱好图像处理的朋友有所帮助。(Binary image processing algorithms are developed set theory in mathematical morphology. Although its basic operation is simple, it can produce complex effects. Commonly used binary image processing operations there are many ways, such as corrosion, expansion, refinement, open operation and closed operation and so on. In this paper, some of these studies to explore, I hope to love image processing friends help.)

平台基于visual studio2015，使用opencv函数实现双边滤波，进行图像处理(bilateral Filter based on opencv)

VisualC++数字图像处理技术详解第2版光盘，第十章(VisualC++ digital image processing technology Detailed Version 2 discs, Chapter X)

包含基于VC++的各种图像、视频处理的源码如图像增强、灰度位图处理、图像平移旋转、bmp2txt等程序源码(Contains various images VC++ based video processing source code, such as image enhancement, grayscale bitmap processing, image translation and rotation, bmp2txt program source code, etc.)

用c++编写的三维图像变换的源代码，也是计算机图像学中经常要学习的一种变换。(Transform the source code written in c++ three-dimensional images, but also often have to learn a transformation in the computer iconography.)

最小二乘图像影像匹配方法，一个很详细的源码例程，有过程有结果，希望对初学者有帮助。高斯解方程，以及最小二乘匹配，里面都很详细。(Squares image image matching method, a very detailed source code routines, the outcome of the process, I hope to help beginners. Gaussian solution of equations, and least squares matching, which is very detailed.)

图像拼接！！此为本人的毕业设计源码，能完整远行，希望能对大家起到帮助。强烈鄙视那些为了骗取下载数而上载一些无关紧要代码甚至是编译错误一堆的卑鄙小人(Image mosaic! ! This is my graduation project source code can be a complete separation, I hope everyone can help. Strongly despise those who cheat in order to download and upload some irrelevant code compiler error or even a bunch of despicable villains)

说明：  显著性检测的代码，是程明明在12年的CVPR的文章，HC,RC的算法源码。(The code for saliency detection is the source code of Cheng Mingming's CVPR article, HC, RC, in 12 years.)

DOG检测算子，能够构造高斯尺度空间，并检测其中的极值，从而得到有价值的特征点位置(DOG detection operator, the Gaussian scale space can be constructed, and tested one of the extremes, resulting in valuable feature position)