boshu

关于sep协议的matlab源代码，很有价值，喜欢的可以收藏，用于ad hoc节点簇的选择(Sep agreement on matlab source code, great value, like the collection can be used for ad hoc choice of cluster nodes)

lvq and neural network

基于RANSAC算法的基础矩阵求解相机的参数(The fundamental matrix based on RANSAC algorithm to solve the camera parameters)

Local correlation-based fingerprint matching

包括图像分析的四部分代码：matlab扩散和高斯函数，线性扩散，线性复扩散，非线性扩散。(It contains four parts： [1]MATLAB function:diffusion.m gauss.m [2]Linear diffusion Applying linear diffusion to images creating linear scale-space. MATLAB code: demo_lin.m Image: haifa1.bmp [3]Linear complex diffusion Applying linear complex diffusion creating Gaussian and Laplacian scale-spaces. MATLAB code: demo_cmplin.m [4]Nonlinear diffusions: Perona-Malik ["Anisotropic diffusion"] Catte et al. regularization of P-M Complex ramp-preserving diffusion Nonlinear edge preserving diffusions.The classical Perona-Malik process: the value of the diffusion coefficient is reduced near edges estimated by the first derivative. Best applied to step edges. A regularized version by Catte et al- the gradient estimation for controlling the process is smoothed by a Gaussian. Proved to be mathematically well-posed. Ramp preserving complex diffusion- best for ramp-type edges. Results are smoother with almost no staircasing effects. MATLAB code: demo_nldif.m Image: ct_scan.bmp)

FFT解NLS的样例代码，程序中使用标准化后的U(FFT solution of the NLS sample code, the use of standardized procedures for the U)

微分方程求解，利用matlab软件，解决微分方程领域各种问题(Solving differential equations using matlab software, field problems to solve differential equations)

Window下c程序设计的初级教程（含代码），c程序员在windows下开发程序应该阅读的第一部资料(Programming windows via C)

matlab一维码识别 利用radon算子做水平方向矫正(radon correct)

数字图像处理，颜色模型，对图像进行颜色转换(Given a color image ‘football.jpg’. 1) Display the three component images: red, green and blue. 2) Perform histogram equalization for the original image in RGB color space, and display the result. 3) Convert RGB to HSV, equalize just V component of HSV image, convert the processing result back to RGB, and then display it. 4) Discuss and compare these results. )