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基于Opencv，使用python语言，以图割算法实现的图像分割。

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s7_300_GRAPH中文编程手册，s7_300_GRAPH中文编程手册

Graph mining and management has become an important topic of research re- cently because of numerous applications to a wide variety of data mining prob- lems in computational biology, chemical data analysis, drug discovery and com- munication networking. Traditional data mining and management algorithms such as clustering, classification, frequent pattern mining and indexing have now been extended to the graph scenario. This book contains a number of chapters which are carefully chosen in order to discuss the broad research issues in graph management and mining. In addition, a number of important applications of graph mining are also covered in the book. The purpose of this chapter is to provide an overview of the different kinds of graph processing and mining tech- niques, and the coverage of these topics in this book.

VLSI Physical Design：From Graph Partitioning to Timing Closure pdf

立体匹配是深度估计的基础。而bp算法和graph cut算法是比较成功的解决立体匹配（全局）的算法，这里有几种经典的基准源代码，以及代码使用方法及部分注释。一些小的改变甚至于可以不需要知道算法步骤即可实现。