zernike

在知道清晰图像和噪声频率特性的情况下，维纳滤波器被看做是一种有效的图像复原技术(In that resolution images and noise frequency characteristics of the case, the Wiener filter is seen as an effective image restoration technique)

网页版物流跟踪，直接登录web服务器进行物流单号查询(Web version logistics tracking, logistics single query)

zftjh:直方图均衡图像灰度化、均衡直方图、图像对比度增强，图像预处理(zftjh: histogram equalization of image gray-scale, balanced histogram, image contrast enhancement, image preprocessing)

最简单的进行两图像的加减和异或，同或，运算。(The simplest operation is to add and subtract two images, and to add and subtract two images.)

车牌识别。系统界面包括车牌图像预处理、定位、矫正、分割、识别等各个步骤的结果显示；显示包括待识别车辆和处理结果图像或数据(License plate recognition. System interface includes license plate image preprocessing, positioning, correction, segmentation, recognition and other steps of the results display; display includes the vehicle to be identified and processing results images or data)

牛顿环的干涉条纹，灰度图，含有反射图样和透射图样(Newton' s rings interference fringes, grayscale, with reflection and transmission pattern pattern)

IDL中用于读取ENVI格式的遥感卫星影像数据的IDL程序(IDL, ENVI format for reading remote sensing image data on the IDL)

遥感影像分类的matlab实现（源码+图像）。以及分类后评价（总体精度、Kappa系数、混淆矩阵）。(Remote Sensing Image Classification of matlab implementation (source code+ images). After the evaluation and classification (overall accuracy, Kappa coefficient, confusion matrix).)

image compression using sphit algorithm

黑塞矩阵（Hessian Matrix），又译作海森矩阵、海瑟矩阵、海塞矩阵等，是一个多元函数的二阶偏导数构成的方阵，描述了函数的局部曲率。黑塞矩阵最早于19世纪由德国数学家Ludwig Otto Hesse提出，并以其名字命名。黑塞矩阵常用于牛顿法解决优化问题，利用黑塞矩阵可判定多元函数的极值问题。在工程实际问题的优化设计中，所列的目标函数往往很复杂，为了使问题简化，常常将目标函数在某点邻域展开成泰勒多项式来逼近原函数，此时函数在某点泰勒展开式的矩阵形式中会涉及到黑塞矩阵。(Hessian Matrix, is a square matrix of the second order partial derivative of the multivariate function, which describes the local curvature of the function. The Hesse matrix was first proposed by the German mathematician Ludwig Otto Hesse in the 19th century and named after it. Hesse matrix is often used in Newton method to solve the optimization problem, using the black plug matrix to determine the extreme function of the extreme value problem.)