PythonCode

说明：  matlab上面自动实现相机校准标定的一个工具库(Matlab toolbox for multiphase calibration can realize automatic calibration)

水平集(level set)的基本思想是将界面看成高一维空间中某一函数ψ（称为水平集函数）的零水平集，同时界面的演化也扩充到高一维的空间中。我们将水平集函数按照它所满足的发展方程进行演化或迭代，由于水平集函数不断进行演化，所以对应的零水平集也在不断变化，当水平集演化趋于平稳时，演化停止，得到界面形状。(The level set (level set) the basic idea is to interface as a higher dimensional space in a function (called a level set function) of the zero level set, and the interface evolution also extended to higher dimensional space. We evolve or iterate the level set function according to the evolution equation it satisfies. Because the level set function keeps evolving, so the corresponding zero level set is also changing. When the level set evolves to a stable state, the evolution stops, and the interface shape is obtained.)

图像的自动拼接和缝补，采用sift算法作为匹配，是学习全景图像拼接的基础，大家可以下载下来看看(Automatic image stitching and sewing, using sift algorithm as a match, is the basis for learning panoramic image mosaic, you can download to see)

说明：  本程序是基于小波和盒维数的车型识别程序，可以选择不同车型，有GUI界面，有图片，有程序，能运行，相信初学者一定能够获益。(This program is based on the wavelet and box dimension of the vehicle type recognition program, you can choose different models, GUI interface, pictures, programs, can run, I believe beginners will benefit.)

小波融合算法，提供了一个实现两幅图像融合的小波算法(wave fusion agrithom)

matlab序贯相似性检测算法源代码。比模板相似性检测算法高效。(Sequential similarity detection algorithm matlab source code.Than template similarity detection algorithm is efficient.)

说明：  单目视觉中的位姿估算代码，基于正交迭代的OI算法，含有主程序和test代码，test代码可以看到位姿估算解的精度(Monocular vision pose estimation code, orthogonal iterative algorithm based OI, containing the main program and test the code, test code can see the accuracy of pose estimation Solutions)

彩色图像处理 格式转化rgb2hsi，hsi2rgb 冈萨雷斯(AN M-function fro converting from RGB TO HSI, HSI to RGB)

信号压缩传感的实现(正交匹配追踪法Orthogonal Matching Pursuit)(The realization of signal compression sensing)

用于脸部识别时，使用基于像素和区域的图像特征核的稀疏表示的识别算法。(When used in face recognition using image recognition algorithm based on feature pixel and regional nuclear sparse representation.)