multi-label graph cut，image segmentation 。

关于图割问题 解决最小割最大流Max-flow/min-cut问题的工具箱

用于图像分割的grabcut, 而且非opencv版本，是c++源码，并有max-flow源码，可以用于其它图求解。 程序支持mask和矩形框两种输入，并附有样图和结果图。详细原理请参考文献："GrabCut" - Interactive Foreground Extraction using Iterated Graph Cuts

完全自研的超级批量视频自动化剪辑工具，效率提升500%。使用异常简单

图割和graph cut 实现的交互式图像分割，实现了图割的交互式分割和两者的结合。对学习图割的人有很大帮助。

A-PDF Page Cut 3.5，带序列号。可以将扫描的pdf双页文档分割成单页。

图割算法，基于连续凸优化的一个变种，绝对能运行，不需要什么动态链接库之类的，纯matlab实现

graph cut matlab 代码 可以运行 能直观看到结果。下载觉不会后悔的。 对理解和使用matlab 函数都有帮助

最大流/最小割算法的简介，理解常用最大流最小割概念的文献，值得学习。 minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style “push-relabel” methods and algorithms based on Ford- Fulkerson style “augmenting paths.” We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.