Title :
A New Scan-Line Algorithm Using Clustering Approach
Author :
Tian, Xiaoguang ; Ma, Yuke ; Hou, Xiaorong
Author_Institution :
Coll. of Autom., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
Correct recognition of the lines is essential for technical drawing understanding. Automation solution is quite difficult due to the limitations of machine vision algorithm. In order to promote development of better technology, according to the fast and high-quality clustering algorithm particle swarm optimization (PSO), a new fast and high-quality line clustering algorithm present in this paper, that consisting of one scan-line connected components processing are clustered and an appropriate measure to recognize the pattern of every line including the dash-line in the drawing paper. The underlying mechanisms are excluding isolated components, a sequential stepwise recovery of components that meet certain continuity conditions and the results presented the node-tree structure that can enhance efficiency of computer. The performance of the algorithm is better in our experiment.
Keywords :
CAD; computational geometry; particle swarm optimisation; pattern clustering; clustering approach; high-quality line clustering algorithm; machine vision algorithm; node-tree structure; particle swarm optimization; scan-line algorithm; sequential stepwise recovery; technical drawing understanding; Appropriate technology; Automation; Character recognition; Clustering algorithms; Data mining; Educational institutions; Hybrid intelligent systems; Machine vision; Particle swarm optimization; Technical drawing; clustering algorithm; particle swarm optimization (PSO); recognition of the lines; recovery of the lines; scan-line algorithm;
Conference_Titel :
Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-0-7695-3745-0
DOI :
10.1109/HIS.2009.129