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在桌面上显示图像，C++Builder精选编程学习源码，很好的学习资料。(Image display on the desktop, C++Builder select learning programming source code, very good learning materials.)

说明：  在ENVI中利用此程序可将多光谱影像拆分为单个波段。(In ENVI, multi-spectral images can be divided into single bands by using this program.)

graph cut 源码实现,yuri Boykov大牛的经典之作(graph cut algorithm matlab implementation)

CBCT图像fdk重建,预投影、投影滤波、重建演示，功能强大(it s about the fdk rec for CBCT，forward projection、filter projection、rec demo)

对多目标航迹跟踪进行关联和融合,采用JPDA（联合概率数据关联算法）对目标跟踪(And the multi-target track tracking is combined and merged)

图像处理基本操作MATLAB原代码，包括各种经典算法，处理过程源码(Basic operation of image processing the original MATLAB code, including a variety of classic algorithms, process source)

单帧图像超分辨率重建。对于学习超分辨率重建很有帮助。(Super-resolution reconstruction of single frame image.It is helpful for learning super-resolution reconstruction.)

SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。 该方法于 1999 年由 David Lowe 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004 年再次经 David Lowe 整理完善后发表于 International journal of computer vision（IJCV） 。截止 2014 年 8 月，该论文单篇被引次数达 25000 余次。(来自百度)(SIFT, namely Scale-invariant feature transform (SIFT), is a description used in the field of image processing. This description has scale invariance and can detect key points in images. It is a local feature descriptor. This method was first published by David Lowe at the International Conference on Computer Vision (ICCV) in 1999 and published in the International Journal of Computer Vision (IJCV) in 2004 after it was sorted out and perfected by David Lowe again. As of August 2014, more than 25,000 citations have been cited. (From Baidu))

老外写的图像分割matlab代码，内有详细的说明(Foreigner to write matlab code for image segmentation, which is described in detail)

C++ 写的一些算法模板 ，比如Kruskal、KMP、欧几里德算法、最大匹配(匈牙利算法)、最小费用最大流、字符串的哈希方法、增广路算法、网络最大流_邻接表等……(C++ write some algorithms template, such as Kruskal, KMP, Euclid arithmetic, maximal matching (Hungary) is proposed, the minimum cost and maximum flow, string hash method, augmented road algorithm, network maximum flow _ adjlink etc......)