LabWindows CVI 3D graph控件编程 可以使用、参考，绘制3D图的基础提高程序~

OpenSceneGraph 3 Cookbook Book Description OpenSceneGraph is an open source 3D graphics application programming interface, used by application developers in fields such as visual simulation, computer games, virtual reality, scientific visualization, and modeling. Familiar with its concepts and APIs, and need to improve your knowledge? This book is here to help. With exactly 100 recipes, it will enrich your experience and take you to the next level. OpenSceneGraph 3 Cookbook will escort you into the world of real-time 3D development with OpenSceneGraph, the world famous 3D graphics engine. The book assumes that you are already familiar with some basic concepts, and provides 100 vivid recipes to demonstrate how to utilize the API flexibly and how to implement some practical aspects. The book is divided into 10 chapters, each of which focuses on one topic. Readers may start from any topic they are interested in. The book includes customizing OpenSceneGraph, designing scene graph, geometry, scene navigation, animations, effects using fixed pipeline and shaders, managing mass data, and miscellaneous discussions. Every recipe will prove to be useful in a practical way or in research. OpenSceneGraph 3 Cookbook gives you a wide insight about 3D programming using OpenSceneGraph, with a set of meaningful and interesting examples.

pls_graph使用手册，讲述该软件的详细操作

Efficient Graph-Based Image Segmentation论文的实现代码，以及给了一个测试样例，在Linux下运行通过

Selective Search论文中部分功能的C++实现，eclipse下的C++工程，直接导入即可。具体内容参考相关文论及本人博客。

谱图理论的英文书

基于Opencv，使用python语言，以图割算法实现的图像分割。

西门子300plc功能流程图编程软件配合仿真软件轻松自学西门子300plc!

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.