RCNN Fast-RCNN Faster-RCNN Mask-RCNN系列论文 RCNN Fast-RCNN Faster-RCNN Mask-RCNN系列论文

快速行进算法（fast marching）完整的运行部分和函数输入变量的说明。采用了三种Fast Marching方法，包括传统的一阶fast marching方法、二阶msfm方法、和matlab工具箱方法。文档说明可参考博客：https://blog.csdn.net/lusongno1/article/details/88409735。

Network Algorithmics --An Interdisciplinary Approach to Designing Fast Networked Devices by George Varghese University of California, San Diego

能够运行 划分的很清楚 输出为聚类图

梯度法中最速下降法采用Matlab编写的

用fast ICA进行混叠信号的分离 没有其他的附带文件，只有一个文件就实现了

Newman快速算法，是一种凝聚算法，基于python的复杂网络库--Networkx实现，有源数据和网络可视化呈现

超拉普拉斯先验的盲去卷积算法论文中的Matlab代码可以运行。

Sethian J A. Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science[M]. Cambridge university press, 1999.

For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data? ” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm that applies priority search on hierarchical k-means trees, which we have found to provide the best known performance on many datasets. After testing a range of alternatives, we have found that multiple randomized k-d trees provide the best performance for other datasets. We are releasing public domain code that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection.