cvprcode

这是一个利用matlab开发的程序，功能是用susan算法提取图像角点的。一个压缩包，程序可用哟。(This is a program developed using matlab, the functional algorithm is susan corner of image. A compact package, the procedure can be used yo.)

shi-tomasi方法角点提取方法。该方法测量结构张量的最小特征值。这代表了Harris和诺贝尔探测器试图近似的理想。哈里斯希望在计算特征时避免计算平方根的计算成本。(Shi-Tomasi corner detector method.The method measure returns the minimum eigenvalue of the structure tensor. This represents the ideal that the Harris and Noble detectors attempt to approximate. Back in 1988 Harris wanted to avoid the computational cost of taking a square root when computing features. This is no longer relevant today!)

基于互相关的图像配准程序，能实现亚像素级的配准(Sub pixel level registration based on cross correlation image registration process)

颜色特征提取，颜色直方图，为matlab 源文件(feature extraction,colorhist,matlab)

基于小波变换的数字图像降噪的源代码，利用改进的小波阈值法进行图像去噪(Digital image noise reduction based on wavelet transform source code, improved wavelet thresholding method for image denoising)

最小二乘图像影像匹配方法，一个很详细的源码例程，有过程有结果，希望对初学者有帮助。高斯解方程，以及最小二乘匹配，里面都很详细。(Squares image image matching method, a very detailed source code routines, the outcome of the process, I hope to help beginners. Gaussian solution of equations, and least squares matching, which is very detailed.)

说明：  基于SURF算法实现图像特征提取与描述，使用RANSAC进行图像细配准(SURF algorithm was used to extract and describe image features, and RANSAC was used for image registration)

通过点云数据的格网化，建立格网分块索引，这是一种有效的点云数据存储方式。( Through the point cloud data grid , the establishment of a grid block index, which is a valid point cloud data storage. )

利用ADMM和小波进行图像恢复 使用ADMM算法的图像恢复模型，并用到了增广拉格朗日函数和快速傅里叶函数(Using ADMM and wavelet to restore image, using ADMM algorithm to restore image model, and using augmented Lagrange function and fast Fourier function.)

说明：  最小噪声分离变换（Minimum Noise Fraction Rotation，MNF Rotation）工具用于判定图像数据内在的维数（即波段数），分离数据中的噪声，减少随后处理中的计算需求量(Minimum Noise Fraction Rotation (MNF Rotation) tool is used to determine the intrinsic dimension (i.e. band number) of image data, separate the noise in data, and reduce the computational requirements in subsequent processing.)