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谱残缺算法显著性检测源程序!!谱残缺算法显著性检测源程序!!(the vision of matlab of special residual of salienp)

压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ILRS算法，对256*256的lena图处理，比较原图和IRLS算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间(Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and IRLS algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using IRLS algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

图形密码源码程序，例程结合易语言特殊功能支持库，调用API函数判断图形图像的点击顺序。(Graphics password source programs, routines combined with special features easy language support library , call the API function to determine whether a graphic image click sequence.)

使用统一资源定位符类URL实现文件下载，同时ImageIO也可以实现(class URL download file)

说明：  生成手写数字图片，一共提供了三种算法，example_1和example_2训练GAN来生成手写数字图片，搭建了一个简单的GAN的网络结构，example_3难度较大(There are three algorithms to generate handwritten digital pictures. Example_1 and example_2 train GAN to generate handwritten digital pictures, and build a simple network structure of GAN. Example_3 is more difficult.)

alpha稳定分布,密度函数图形，这较少出现。(alpha-stable distribution，picture of pdf,it s very rare)

说明：  一、主要研究内容 1.基本内容 本设计目标在于利用Matlab强大的图像处理能力和实用便捷的编程方法，通过处理包含人脸的视频帧系列图像，灰度积分投影技术的眼睛定位方法，进而利用perclos计数，计算眨眼率，从而得到比较准确的疲劳状况。包括lun文初稿撰写。 2.具体要求 本设计基于灰度积分投影技术的眼睛定位方法，再结合perclos技术。首先通过图像预处理技术得到灰度分配较为均匀的图像,然后分别利用水平和垂直灰度积分投影曲线结合人脸的结构特征找到眼睛的位置坐标,实现了准确的眼睛定位,通过perclos技术技术眨眼率，根据先验值得到是否疲劳。 二、算法原理 视频输入——预处理去除非人脸区域——人脸定位——人眼定位（灰度积分投影）——眼睛张合度——perclos技术统计——输出结果 三、GUI界面设计 [需要有一定编程功底](1. Basic contents The goal of this design is to use the powerful image processing ability of MATLAB and the practical and convenient programming method, through processing the video frame series images including face, the eye location method of gray integral projection technology, and then using PERCLOS count to calculate the blink rate, so as to get a more accurate fatigue condition. Including the first draft of Lun.)

Large Deviations for Gaussian Queues - Modelling Communication Networks - Michel Mandjes

说明：  实现多视两幅图像的surf特征点提取与匹配(Achieve surf feature point extraction and matching of two multi-view images)

这是一个数字图像处理的程序，能够实现目标检测与跟踪，事项了EM算法、是对原有的算法改进。(This is a program of digital image processing, target detection and tracking can be achieved, the matter of the EM algorithm, the improved algorithm is the original.)