canny

说明：  该压缩包内含有基于梯度金字塔变换的多模态图像融合方法的MATLAB代码，可以直接运行，得到融合结果。另外，压缩包内包括了多组已配准、待融合的图像示例，具体包括红外图像与可见光图像、CT与MR图像，多聚焦图像。本人还对代码进行了必要的解释。推荐下载(The compression package contains matlab code of multimodal image fusion method based on gradient pyramid transformation, which can be run directly to get fusion results. In addition, the compressed package includes several groups of images that have been registered and to be fused, including infrared images and visible images, CT and MR images, and multi focus images. I also gave the necessary explanation to the code. Recommended download.)

检测前跟踪，使用phd方法，phd实现是检测前跟踪是最新技术，可以提高跟踪性能(Detected before tracking method using phd, phd implementation is detected before tracking is the latest technology that can improve the tracking performance)

VC计算机图形学原理及算法教程 VC计算机图形学入门教程(VC theory and algorithms of computer graphics computer graphics tutorial Tutorial VC)

基于matlab的自动报靶系统，文件内包括matlab源码以及GUI图形界面，以及模拟射击的靶纸图像，程序运行无误，靶纸环数识别准确。(Automatic target scoring system based on MATLAB)

修复破损图片，图像修复序列-Lowrank模型(Repair damaged picture, image repair sequence -Lowrank model)

数字散斑相关计算中，亚像素位移检测，梯度算法(sub-pixel, gradient method,DSCM)

说明：  数字图象处理的文献资料，可以很好的处理边缘检测的文献(Digital image processing literature, can handle edge is very good detection literature)

说明：  标准三步搜索法，用matlab实现，经过实验效果还可以(Standard three-step search method, using matlab to achieve, after the experimental effect can also be)

burg距离方位分别外推超分辨算法，实现对稀疏数据的超分辨成像。(burg extrapolation distance and orientation were super-resolution algorithm to achieve the super-resolution imaging sparse data.)

cvpr2012_oral On Multiple Foreground Cosegmentation(In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur over the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In part)