floating clouds, which is the source code

1)具体功能为:鼠标绘制折线,给出折线的缓冲区宽度,提取出折线的平行线. 2)本程序是利用地图代数中距离变换的方法(栅格方法),提取折线的平行线.和笔者前一阵子上传的折线的平行线算法(矢量方法)可做对比 3)注:矢量方法中没有考虑垂直的特殊情况,增加个判断条件便可消除. -1) the specific features as follows: the mouse line drawing, broken line give the width of the buffer, to extract the broken line parallel line .2) This procedure is the use of map algebra in the distance transform method (grid method), Extraction of broken parallel lines. and the author of the line long ago From the parallel line algorithm (vector method) can be compared 3) Note: The vector method does not consider the special circumstances of the vertical, increasing the conditions of a judge could be removed.

C语言开发的Opencv 移动物体识别的程序以及OpenCV的基础教程-C language development Opencv moving object identification process and the basis for OpenCV Tutorial

全局Retinex彩色图像恢复,VC++实现，效果不错，还原真实色彩，与直方图均衡化比较-Global Retinex color image restoration, VC++ to achieve good results, restoring true colors, compared with the histogram equalization

很好的聚类代码，可以用于对图像数据聚类。-A good clustering code that can be used to image data clustering.

一个用于转换bmp 文件格式的程序， 可转换成单色的bmp图像。-One for the bmp file format conversion process, can be converted into monochrome bmp images.

双正交小波构造原理祥述.在图象处理的编程过程中起到指导作用-Biorthogonal Wavelet Construction Principle Xiang references. In the image processing program to play a guiding role in the process of

基于OpenCV的图像匹配程序，可达到亚象素，对图像匹配有很大的帮助！-OpenCV-based image matching procedure, can achieve sub-pixel of the image matching has a great help!

近邻法的matlab 程序,采用1近邻法,多分剪辑的方式,有助于对近邻法的理解-Matlab neighbor law procedures, the use of one neighbor method, multi-way sub-clips, contribute to the understanding of neighbors Law

基于matlab的目标跟踪和识别的源代码，很不错。可以-Matlab-based target tracking and identification of the source code, is pretty good. Can

SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。后来Y.Ke将其描述子部分用PCA代替直方图的方式，对其进行改进。 2 SIFT 主要思想 SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。 -a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images.