摘要
针对目前工程图纸矢量化过程中圆弧检测准确率不高、检测时间过长等问题,提出一种基于切线段匹配的快速圆弧检测算法。首先,该方法找出可能位于圆外边界上八方向(0,π/4,π/2,…,7π/4)与圆相切的线段,并记录在切线集合中;然后,对已找到的切线段进行两两配对,估算圆心半径范围,得到候选圆集合;最后,对获取的候选圆集合进行数据合并,对合并后的每一个候选圆进行跟踪检测,最终确定它是一个圆还是一个弧。在切线段寻找过程中进行匹配,对已确定为圆的候选圆,在切线段集合中去除与该圆相对应切线段,有效减少了匹配次数。在对比实验中,所提算法的平均识别率达到了97.250%,平均检测时间为12.290 s,比随机抽样一致性(RANSAC)算法和有效投票算法(EVM)的平均识别率更高,平均检测时间更短。实验结果表明,所提算法能够有效地对低噪声图像中弧长大于1/8圆周长的圆弧进行检测,同时能提高检测准确率、缩短检测时间。
Focusing on the low accuracy and long detection time of arc detection in engineering drawing vectorization,a fast arc detection algorithm based on tangent lines matching was proposed. Firstly,tangent lines on the circle outer boundary were detected from eight directions（ 0,π /4,π /2,…,7π /4） and were added in tangent lines set. Secondly,the tangent lines in the set were paired up,and the center and radius of circles were estimated to obtain circle candidate set. Finally,tracing detection was performed for every candidate circle after merging data of circle candidate set,and every candidate circle was ascertained as a circle or an arc. The paring process was executed during the tangent lines searching,and the number of pairing was effectively reduced by removing the relative tangent lines of the identified candidate circle. In the contrast experiments with RANdom SAmple Consensus（ RANSAC） algorithm and Effective Voting Method（ EVM）,the proposed method reached average detection accuracy of 97. 250%,and the average detection time was 12. 290 s,which were better than those of the comparison methods. The experimental results illustrate that the proposed method can effectively detect the arc which length is greater than1 /8 circumference in low noise image,improve the accuracy of detection and shorten the detection time.
出处
《计算机应用》
CSCD
北大核心
2016年第4期1126-1131,共6页
journal of Computer Applications
基金
辽宁省自然科学基金资助项目(2013020012)~~
关键词
工程图纸矢量化
圆弧检测
八方向
扫描线
切线段
engineering drawing vectorization
arc detection
eight-direction
scanning line
tangent line
