首页 > 论文 > 光学学报 > 38卷 > 8期(pp:815016--1)

基于改进的概率Hough变换的直线检测优化算法

Line Detection Optimization Algorithm Based on Improved Probabilistic Hough Transform

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

针对概率Hough变换耗费大量内存以及直线端点搜索容易受到网状聚集点干扰的缺陷,提出一种基于概率的局部Hough变换优化算法。将边界分为有序和无序两类,前者通过随机抽取采样点并结合其相邻点进行直线搜索,后者采用在随机抽取点周围建立感兴趣区域并进行局部Hough变换,检测到直线后进行全局搜索并实时修正直线斜率,对因网状聚集点产生的错误直线采用间隔计数和限制总间隔长度的方法进行排除。使用500张图片进行实验验证,算法耗时均低于概率Hough变换耗时的1/3,且对网状聚集边界点的直线错检具有较高抵抗性,检测结果比概率Hough变换直线检测更加准确,内存消耗减少超过90%以上。

Abstract

Aiming at the drawbacks that for probabilistic Hough transform it consumes a lot of memory and the line endpoints searching is vulnerable to interference from reticular aggregation points, a probability-based local Hough transform optimization algorithm is proposed. The edge is classified into two categories: sortable and non-sortable. For the former, sampling points are randomly picked and combined with their adjacent points for straight line searching. For the latter, the local probabilistic Hough transformation is carried out in the region of interest which is established around the random edge point, the endpoints are searched after the line is detected and the slope is fixed in real time. The error lines resulting from mesh aggregation points are excluded by the interval counting and the total interval length limit method. Experiments were carried out by 500 images. The proposed algorithm consumes less than 1/3 of the time of the probabilistic Hough transform, and it is highly resistant to line mis-detection of meshed aggregation edge points. Line detection is more accurate and memory consumption is reduced by more than 90%, compared with the probabilistic Hough transform.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.9

DOI:10.3788/aos201838.0815016

所属栏目:“机器视觉检测与应用”专题

收稿日期:2018-03-20

修改稿日期:2018-04-07

网络出版日期:2018-05-08

作者单位    点击查看

刁燕:四川大学制造科学与工程学院, 四川 成都 610065
吴晨柯:四川大学制造科学与工程学院, 四川 成都 610065
罗华:四川大学制造科学与工程学院, 四川 成都 610065
吴必蛟:四川大学制造科学与工程学院, 四川 成都 610065

联系人作者:刁燕(diaoyan2@163.com)

【1】Shapiro S D. Feature space transforms for curve detection[J]. Pattern Recognition, 1978, 10(3): 129-143.

【2】Duda R O, Hart P E. Use of the Hough transformation to detect lines and curves in pictures[J]. Communications of the ACM, 1972, 15(1): 11-15.

【3】Zhao L J, Liu E H, Zhang W M, et al. Feature extraction of target based on global information[J]. Acta Optica Sinica, 2014, 34(4): 0415001.
赵连军, 刘恩海, 张文明, 等. 利用全局信息提取靶标特征的方法[J]. 光学学报, 2014, 34(4): 0415001.

【4】Jiang G Q, Ke X, Du S F, et al. Crop row detection based on machine vision[J]. Acta Optica Sinica, 2009, 29(4): 1015-1020.
姜国权, 柯杏, 杜尚丰, 等. 基于机器视觉的农田作物行检测[J]. 光学学报, 2009, 29(4): 1015-1020.

【5】Yan H R, Yang M S. Line extraction based on improved hough transform[J]. Infrared Technology, 2015, 37(11): 970-975.
闫怀仁, 杨慕升. 基于改进的Hough变换的直线提取算法[J]. 红外技术, 2015, 37(11): 970-975.

【6】Zhang G Y, Cheng Y Y, Zhu H. Detection of linear target based on improved hough transform[J]. Computer Engineering and Design, 2014, 35(2): 536-540.
张国英, 程益钰, 朱红. 基于改进Hough变换的线性目标检测[J]. 计算机工程与设计, 2014, 35(2): 536-540.

【7】Xu L, Oja E, Kultanen P. A new curve detection method: Randomized Hough transform (RHT)[J]. Pattern Recognition Letters, 1990, 11(5): 331-338.

【8】Liu T, Chen H, Shen M, et al. Effective echo extraction for space debris laser ranging using randomized Hough transform[J]. Chinese Journal of Lasers, 2016, 43(4): 0408002.
刘通, 陈浩, 沈鸣, 等. 随机Hough变换提取空间碎片激光测距有效回波[J]. 中国激光, 2016, 43(4): 0408002.

【9】Yan R, Zhang L C, Zhang Y S, et al. Tricot lace real-time recognition method based on feature recognition[J]. Laser & Optoelectronics Progress, 2015, 52(11): 111002.
鄢然, 张李超, 张宜生, 等. 基于特征识别的经编布花边实时识别算法[J]. 激光与光电子学进展, 2015, 52(11): 111002.

引用该论文

Diao Yan,Wu Chenke,Luo Hua,Wu Bijiao. Line Detection Optimization Algorithm Based on Improved Probabilistic Hough Transform[J]. Acta Optica Sinica, 2018, 38(8): 0815016

刁燕,吴晨柯,罗华,吴必蛟. 基于改进的概率Hough变换的直线检测优化算法[J]. 光学学报, 2018, 38(8): 0815016

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF