Special-Project

做项目时写的关于相机图像采集、控制、图像处理、坐标计算、坐标显示等功能的小软件(Do item written on the camera image acquisition, control, image processing, coordinates of the calculation, the coordinates show features such as small software)

说明：  位图分析转换软件 可以分析8，16，24，32位BMP图 输出可选16位，18位，24位RGB格式 同时支持串行下载(Bitmap analysis of 8,16,24,32-bit conversion software can map out the optional 16-bit BMP, 18 bit, 24-bit RGB format also supports serial download)

这是一个关于鲁棒的多传感器图图像配准程序的算法可直接使用。 (This is a robust multi-sensor chart image registration procedures of the algorithm can be used directly.)

使用QT编程实现2D图像处理的各功能，包括彩色图转灰度、二值化、二值的形态学操作、灰度的形态学操作等等。(Using QT programming each 2D image processing functions, including color conversion grayscale, binary, binary morphological operations, grayscale morphological operations and so on.)

使用OpenCV实现特征点的查找以及两幅图像中的特征点匹配(Use the function findHomography to find the transform between matched keypoints. Use the function perspectiveTransform to map the points)

光线跟踪，计算机图形学的实验，实现的是消隐，光线之间的遮挡等(Ray-tracing, computer graphics experiments, realize that the hidden light, such as between the block)

利用OpenCV实现单幅图片对于手指的精确识别(Single image using OpenCV to achieve precise identification of the finger)

基于轮廓来寻找流动部分，效果不是很好，但是还可以拿来用(Based on the outline to find the flow part, the effect is not very good, but also can be used with)

VC++图像平滑处理+程序源码，实现图象的平滑（去噪声），锐化功能，测试时请在C盘下放一个测试用的Bmp图片，路径：C: est.bmp，没有的话没法测试。如上图所示是水平平滑处理，可以对比下，不过感觉颜色失真了，有兴趣的看一下。命令行编译过程如下： 　　vcvars32 　　rc bmp.rc 　　cl smooth.c bmp.res user32.lib gdi32.lib(VC++ image smoothing+ program source code, to achieve a smooth image (to noise), sharpening function in the C drive when testing a test of decentralization Bmp picture, path: C: test.bmp, no words can not test . Is shown above the level of smoothing, you can compare the next, but the feeling of color distortion, interested look. Command-line compiler as follows: vcvars32 rc bmp.rc cl smooth.c bmp.res user32.lib gdi32.lib)

这是一个比较完整的KPCA算法，通过这个算法可以快速的找到主要的特征向量进行主成分分析。(This is a relatively complete KPCA algorithm, this algorithm can quickly find the main feature vector principal component analysis.)