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.

James A. Sethian. Level Set Methods and Fast Marching Methods. Cambridge University Press (1999). 评点：这是另外一个创始人Sethian的作品，与Osher的书互有侧重，互相补充，这本书更偏重于Fast Marching Methods, 非结构化网格，涉及的应用领域更广泛。

稀疏贝叶斯学习大师M.Tipping的matlab代码，可实现用相关向量机快速算法分类及回归，“Fast Marginal Likelihood Maximisation for Sparse Bayesian Models”原文献

何凯明 引导滤波 以及 快速引导滤波 相关论文、PPT以及代码。 引导滤波：即需要引导图的滤波器，引导图可以是单独的图像或者是输入图像，当引导图为输入图像时，引导滤波就成为一个保持边缘的滤波操作，可以用于图像重建的滤波。

该压缩包是关于fast角点检测的c++实现的，适用于初学者。

This book presents a comprehensive overview of the recently developed L1 adaptive control theory, including detailed proofs of the main results. The key feature of the L1 adaptive control theory is the decoupling of adaptation from robustness.

FAST角点检测的最初始的英文原文，Edward Rosten 和 Tom Drummond 在2006年发表。我的博客里有详细的介绍这个算法，欢迎相互交流>_< http://blog.csdn.net/tostq