MatPIV161

SAR成像处理的过程是从回波数据中提取目标区域散射系数的二维分布，本质上是一个二维相关处理过程,因此最直接的处理方法是对回波进行二维匹配滤波，但其运算量很大，再加上SAR的数据率本来就高，这使得实时处难于实现。通常，可以把二维过程分解成距离向和方位向两个一维过程，Range-Dopper Algorithm（简称RD算法）(SAR imaging process is two-dimensional distribution of the scattering coefficient of the target area extracted from the echo data, in essence, is a two-dimensional process, the most direct approach is two-dimensional matched filtering of echo, its computationalthe great plus on SAR data rate was high, which makes real-time processing is difficult to achieve. Typically, two-dimensional process can be broken down into the range and azimuth to a two-dimensional process, the Range-Dopper Algorithm. (Referred to as the RD algorithm))

这块程序是用来求取圆面和矩形面积的，分为几个子程序。(This procedure is used to strike a round face and a rectangular area, divided into several subroutines.)

最小二乘匹配，图像匹配算法，需要初始匹配坐标，可以达到亚像素精度(least squares matching, image matching algorithm, the initial need to match coordinates, can reach sub-pixel accuracy)

运用相位相关法对图像进行匹配，并通过图形形象地显示相关系数位置。对图像的亮度变换不敏感，相关峰尖突出，有较高的配准精度。(fft、ifft、phase correlation)

检测鼠标信息，C++Builder精选编程学习源码，很好的参考资料。(Detecting mouse information, C++Builder select learning programming source code, a good reference.)

使用split bregman算法实现的压缩感知(Compressed sensing implemented by using split Bregman algorithm)

新浪邮箱注册易语言源码，程序结合易语言第三方云外归鸟的图像处理支持库和易语言模块彗星HTTP应用模块.ec，实现新浪邮箱的注册。(Sina mail registration easy language source code , the program combined with easy language to third parties outside the cloud of birds of image processing and easy language support library module Comet HTTP application modules .ec, realize Sina-mail registration .)

基于RADON变换的。运动模糊处理，其中程序请按说明使用(Based on the RADON transform. Motion blur processing, in which procedures follow the instructions to use)

多维尺度不变性的Ｃ＋＋实现，必须联合ＩＴＫ一起用，享受吧(Multi-dimensional scale invariance of the C to achieve, we must co-itk together, and enjoy it)

用于实现高密度人群聚合检测 包含：边缘检测、灰度共生矩阵计算、SVM特征分量的分类(For achieving high-density population polymerization detector comprises: edge detection, gray level co-occurrence matrix calculation, classification SVM characteristic component)