tuxiang

说明：  基于边缘特征的SAR图像与光学图像匹配，测度为Hausdorff距离(SAR images based on edge features and optical image matching, for the Hausdorff distance measure )

说明：  本课题的主要内容是图像预处理,它主要从摄像头中获取人脸图像然后进行处理,以便提高定位和识别的准确率.该模块主要包含光线补偿、图像灰度化、高斯平滑、均衡直方图、图像对比度增强，图像预处理模块在整个系统中起着极其关键的作用，图像处理的好坏直接影响着后面的定位和识别工作,内有源代码和全部论文资料(this issue is the major content of image preprocessing, mainly from the camera to obtain images Face then, in order to improve the recognition and positioning accuracy. The module consists mainly of light compensation, Grayhound, Gaussian smoothing, balanced histogram, image contrast enhancement, image pre- processing module in the system plays a crucial role in image processing will have a direct impact behind the positioning and identification, within Active code and all papers information)

说明：  人眼定位的matlab代码，使用模板匹配的方法实现(Eye location matlab code, using the template matching method)

指导滤波，类似于双边滤波的效果，但是速度很快(guided filter)

使用matlab调用opencv做成的adaboost人脸检测DLL模块，在usb摄像头采集的视频序列中检测人脸并实时显示， 实时性比C低。 下载后可以直接在matlab运行，不要改动文件夹内的文件相对位置。(Opencv matlab call made using adaboost face detection DLL module, usb camera capture video sequences of face detection and real-time display, real-time lower than C. Download can be run directly in matlab, do not change the file folder relative position.)

视频异常行为检测，可以实现，使用matlab编程(Video abnormal behavior detection can be achieved using matlab programming)

多尺度快速角点检测算法（FAST).Adaptive and generic corner detection based on the accelerated segment test. 出处 Computer Vision–ECCV 2010(Multi-scale fast corner detection algorithm (FAST) Adaptive and generic corner detection based on the accelerated segment test. Computer Vision-ECCV 2010)

程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量(Programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)

何凯明暗通道先验法进行单幅图像去雾的实现。 严格按照每一步编程。步骤清晰，代码易懂，更改图片路径后便可执行。(Make a single image to achieve HE dark fog channel priori method. In strict accordance with each step programming. Step clear, easy to understand code, change the image path after the executable.)

非精确ALM解决PCA算法的例子，用后效果不错，发上来分享。(This matlab code implements the inexact augmented Lagrange multiplier method for Robust PCA.)