汉化包

极化SAR图像多纹理最大似然处理滤波算法，可以生成处理后的四个通道图像数据。(Multi-polarization SAR image texture filtering deal with maximum-likelihood algorithm, can be generated after the deal with the four-channel image data.)

1.图像尺度转换，降低图像分辨率，采用局部平均法、中值法；2.图像质量评价：均值、标准差、信息熵、平均梯度(1.Image scaling, reducing image resolution, using a local average、median method；2.Image quality assessment: the mean, standard deviation, entropy, average gradient )

SIFT匹配算法，能够提取出具有尺度不变性的特征点，且能够对两幅图像进行鲁棒的匹配。(SIFT matching algorithm, able to extract with the scale invariance of the feature points, and the two images to be able to match robust.)

在图像去卷积算法引入先验模型的应用,主要是建立马尔可夫随机场先验模型.很大程度上改进了去卷积的效果,更多的保持了图像的边缘特征.(In image deconvolution algorithm Application of the introduction of a priori model, mainly the establishment of Markov random field a priori model. A large extent, improved the effectiveness of deconvolution, more to maintain the image of the edge feature.)

快速非局部均值，用来消除噪声，效果良好，适合于细节较多的图像(Fast nonlocal means is used to eliminate noise and achieve good results, which is suitable for more detailed images.)

在VC6.0环境下，借助openCV函数库，对视频中的人脸进行检测，并实时跟踪，然后用Haar特征的分类器定位人脸，并检测瞳孔存在与否，提取关于眼睛的疲劳信息(In VC6.0 environment, with the aid of openCV function library, to detect human faces in video, and real-time tracking, positioning and use characteristics of Haar classifier faces, and examine the pupil existence, to extract the information about the fatigue of the eye)

菲涅耳透镜成像的matlab模拟程序代码(modified off-axis refrence beam CGH)

该函数用于简单背景下运动目标检测和轮廓提取，虽为简易实现，但效果不错，可根据个人需要添加代码(This function for a simple moving object detection and contour extraction, although it is easy to achieve, but good results can be based on individual need to add code)

说明：  编程实现对bmp图像最大值，最小值，标准差，熵，各灰度值频率的计算(Programming to calculate the maximum value, minimum value, standard deviation, entropy and gray value frequency of BMP image)

球面坐标定位法校正鱼眼图像畸变，使用visual studio 2008 编写，需要OpenCV的支持(Spherical coordinates locating fish eye image distortion correction, prepared using visual studio 2008, requires the support of OpenCV)