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graphcut的最新改进，在网格上应用graphcut，作者将其称为gridCut,此压缩包内含文章与源代码，出自CVPR2012,关注计算机视觉的同学可以详细参考，代码已调试通过(the new advance of graphcut, named gridCut, derived from CVPR 2012, useful to guys on ComputerVision.The code is runnable.)

一个非常有用的超像素算法，用于图像分割,文件夹包含文章,采用matlab和c混编(a very useful superpixels algorithm，can be used for image segmentation,the file contatins paper,the matlab and c are mixed for exeution)

多目标检测与跟踪算法的实现，matlab程序，各个函数分开写的，便于调用(Many target detection and tracking algorithm, the realization of the matlab, each function, easy to call the write it separately )

利用非线性结构张量处理一些图形问题，能够有效的解决和优化现成的处理方法(In this article, we introduce nonlinear versions of the popular structure tensor, also known as second moment matrix. These nonlinear structure tensors replace the Gaussian smoothing of the classical structure tensor by discontinuity-preserving nonlinear diffusions)

利用matlab控制摄像头，对图像实时进行二值灰度处理(Using MATLAB to control the camera, the image is processed with two value gray in real time)

很好的流形学习，处理高维数据，降维操作，里面含有LLE，PCA，Dijstra,Iosmap等等(Good manifold learning, processing high dimensional data, dimensionality reduction operations, including LLE, PCA, Dijstra, Iosmap, etc.)

一篇介绍粒子滤波的资料，介绍的很详细，适合研究粒子滤波的初学者，希望对大家有帮助(Introduce a particle filter information on the very detailed study of particle filter is suitable for beginners)

压缩感知中利用增广拉格朗日方程解最小稀疏正则化的恢复算法(DAL solves the dual problem of (1) via the augmented Lagrangian method (see Bertsekas 82). It uses the analytic expression (and its derivatives) of the following soft-thresholding operation， which can be computed for L1 and grouped L1 (and many other) sparsity inducing regularizers. If you are interested in our algorithm please find more details in our technical report or in my talk at Optimization for Machine Learning Workshop (NIPS 2009).)

近两年关于图像检索的重要参考文献，包括IEEE和国内CNKI上发表的顶级期刊(Image Retrieval over the past two years on the importance of references, including the IEEE and domestic CNKI top journals published)

snake模型寻找图像边界，以及改进的GVF算法(snake model for image border, and to improve the algorithm GVF)