A New Scan-Line Algorithm Using Clustering Approach

Xiaoguang Tian, Yuke Ma, X. Hou
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引用次数: 2

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
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一种新的聚类扫描线算法
对线条的正确认识对于理解技术图纸是至关重要的。由于机器视觉算法的限制,自动化解决相当困难。为了促进更好的技术发展,根据快速、高质量的聚类算法粒子群优化(Particle Swarm Optimization, PSO),本文提出了一种新的快速、高质量的直线聚类算法,该算法由一条扫描线相连的组件处理聚类,并采取适当的措施来识别图纸上包括虚线在内的每条直线的模式。其基本机制是排除孤立组件,对满足一定连续性条件的组件进行顺序逐步恢复,并给出了提高计算机效率的节点树结构。在我们的实验中,该算法的性能较好
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