GRAPPA

说明：  遥感图像的多光谱目标识别，相似度方法实现分类(The multi-spectral remote sensing image object recognition, similarity classification method)

在张正友的基于平面方格的摄象机标定中,单应矩阵是关键的一步,本程序是效率精度比较高的源码(Zhang is the Friends of the plane on the box camera calibration alone should matrix is a crucial step, The procedure is relatively high efficiency accuracy of source)

一种基于背景减法和帧差的运动目标检测算法(Based on background subtraction and frame difference moving target detection algorithm)

matlab 高光谱处理工具箱 hyperspecreleasesmatlab_hyperspectral_toolbox_v0.07.zip( Matlab Hyperspectral Toolbox Copyright 2008-2012 Isaac Gerg ------------------------------------------------------------------------- A Note on Notation Hyperspectral data is often expressed many ways to better describe the mathematical handling of the data mainly as a vector of pixels when referring to the data in a space or a matrix of pixels when referring to data as an image. ------------------------------------------------------------------------- Dependencies FastICA- http://www.cis.hut.fi/projects/ica/fastica/code/dlcode.shtml ------------------------------------------------------------------------- Functions Reading/Writing Data Files hyperReadAvirisRfl- Reads AVIRIS .rfl files hyperReadAvirisSpc- Read AVIRIS .spc files hyperReadAsd- Reads ASD Fieldspec files. (.asd, .000, etc) Data Formatting hyperConvert2D- Converts data from a 3D HSI data cube to a 2D matrix hyperConvert3D- Converts data fro)

舌像的梯度提取，然后进行分水岭分割，最后进行邻近区域合并，得到舌体图像。(tongue-image s watershed segmentation based on its gradient change.)

contourlet对LENA512灰度图的LP,DEF分解与系数重构代码,经过调试.(Contourlet LENA512 grayscale of the LP, DEF coefficient decomposition and reconstruction of the code after debugging.)

基于K-means的优化聚类中心的图像分割算法，改进了FCM算法的缺点(Based on the optimization of K-means clustering center image segmentation algorithm to improve the shortcomings of the FCM algorithm )

Pyramid methods in image processing

Harris，SURF特征点提取匹配算法，两种算法实例比较(Harris, SURF feature point extraction matching algorithm compares two algorithms examples)

RPCA (Robust Principal Component Analysis)是目前用于矩阵填充、图像去噪的最有效的优化方法。目前最有效的算法是ALM（Augmented Lagrange Multiplier）。ALM分为Exact ALM和Inexact ALM。 该代码是Inexact ALM，收敛速度比Exact ALM快！(RPCA (Robust Principal Component Analysis) is used for matrix filling, image denoising. It is currently the most effective optimization method. Currently the most effective method is ALM (Augmented Lagrange Multiplier). There re 2 kinds of ALM: Exact ALM and Inexact ALM. The code is Inexact ALM, faster convergence speed than the Exact ALM!)