Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2015, Vol. 55 Issue (8): 921-926    
  机械工程 本期目录 | 过刊浏览 | 高级检索 |
基于高效Hough变换的圆柱特征检测方法
杨向东, 芮晓飞, 谢颖
清华大学 机械工程系, 北京 100084
Efficient Hough transform based cylinder feature detection algorithm
YANG Xiangdong, RUI Xiaofei, XIE Ying
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
全文: PDF(1160 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 椭圆Hough变换方法是特征提取领域重要的研究课题之一。为提高算法的精确度和时效性, 提出了高效Hough变换(EHT)圆柱特征检测方法。该方法适用于检测圆柱特征中的椭圆轮廓, 利用单个参数的近似值, 估计其他参数的近似值。为控制算法误差, 提出了EHT的失效模型, 给出了预置参数的安全误差范围。对程序生成的椭圆图像进行检测实验, 证实了EHT方法的有效性。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
杨向东
芮晓飞
谢颖
关键词 Hough变换特征提取椭圆检测圆柱    
Abstract:Ellipse Hough transform method is a significant research topic in the field of feature extraction. Efficient Hough transform (EHT) is proposed to improve the algorithm's accuracy and timeliness. This method is suitable for detecting the ellipse contour in cylindrical feature. The algorithm mainly consists of estimating other parameters' approximation by single parameter's approximation. EHT's failure model is proposed to give the security error range of preset parameter. Synthetic images of ellipse contour are tested to demonstrate EHT's effectiveness.
Key wordsHough transform    feature extraction    ellipse detection    cylinder
收稿日期: 2014-12-30      出版日期: 2015-09-30
ZTFLH:  TP391.4  
引用本文:   
杨向东, 芮晓飞, 谢颖. 基于高效Hough变换的圆柱特征检测方法[J]. 清华大学学报(自然科学版), 2015, 55(8): 921-926.
YANG Xiangdong, RUI Xiaofei, XIE Ying. Efficient Hough transform based cylinder feature detection algorithm. Journal of Tsinghua University(Science and Technology), 2015, 55(8): 921-926.
链接本文:  
http://jst.tsinghuajournals.com/CN/  或          http://jst.tsinghuajournals.com/CN/Y2015/V55/I8/921
  图1 油井井口圆柱特征与关键尺寸
  图2 理想椭圆轮廓的主要参数
  图3 椭圆轮廓的坐标变换
  图4 椭圆的极坐标表示
  图5 椭圆半长轴放大时的突变形式
  图6 椭圆半长轴缩小时的突变形式
  图7 误差突变前椭圆
  图8 误差突变后椭圆
  图9 均匀角密度椭圆轮廓点
  表1 不同半长轴与半短轴的EHT检测椭圆误差突变情况
[1] Hough P V. A method and means for recognizing complex patterns: USA, 3069654 [P]. 1962.
[2] LU Wei, TAN Jinglu. Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT) [J]. Pattern Recognition, 2008, 41(4): 1268-1279.
[3] Nair P S, Saunders A T. Hough transform based ellipse detection algorithm [J]. Pattern Recognition Letters, 1996, 17(7): 777-784.
[4] Mclaughlin R A. Randomized Hough transform: Improved ellipse detection with comparison [J]. Pattern Recognition Letters, 1998, 19(3-4): 299-305.
[5] Chia A Y S, Leung M K H, How-Lung E, et al. Ellipse detection with Hough transform in one dimensional parametric space [C]// ICIP 2007. IEEE International Conference on. San Antonio, TX, USA: IEEE, 2007: 333-336.
[6] XIE Yonghong, JI Qiang. A new efficient ellipse detection method [C]// Pattern Recognition, 2002. Proceedings. 16th International Conference on. Quebec, Canada: IEEE, 2002: 957-960.
[7] CHENG Zhiguo, LIU Yuncai. Efficient technique for ellipse detection using restricted randomized Hough transform [C]// Information Technology: Coding and Computing, 2004. Proceedings. International Conference on. Las Vegas, Nevada, USA: IEEE, 2004: 714-718.
[8] Chien C F, Cheng Y C, Lin T T. Robust ellipse detection based on hierarchical image pyramid and Hough transform [J]. J Opt Soc Am A Opt Image Sci Vis, 2011, 28(4): 581-589.
[9] HAN Fei, GUO Yanling, WANG Lili. A new ellipse detector based on Hough transform [C]// Information and Computing Science, 2009. Second International Conference on. Manchester: IEEE, 2009: 301-305.
[10] HE Kaiming, SUN Jian, TANG Xiaoou. Single image haze removal using dark channel prior [J]. Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353.
[11] 段汝娇, 赵伟, 黄松岭, 等. 一种基于改进Hough变换的直线快速检测算法 [J]. 仪器仪表学报, 2010(12): 2774-2780. DUAN Rujiao, ZHAO Wei, HUANG Songling, et al. Fast line detection algorithm based on improved Hough transformation [J]. Chinese Journal of Scientific Instrument, 2010(12): 2774-2780. (in Chinese)
[12] Park S G, Kim S Y, Kim J Y, et al. Finding the information of the ellipse from the optical Hough transform [C]// International Society for Optics and Photonics. Orlando, Florida: AeroSense, 2000: 352-363.
[13] 杨全银. 基于Hough变换的图像形状特征检测[D]. 山东大学, 2009.YANG Quanyin. Shape features detection based on Hough transform [D]. Shandong: Shandong University, 2009. (in Chinese)
[1] 肖熙, 周路. 基于k均值和基于归一化类内方差的语音识别自适应聚类特征提取算法[J]. 清华大学学报(自然科学版), 2017, 57(8): 857-861.
[2] 焦智灏, 杨健, 叶春茂, 宋建社. 基于散射成分一致性参数的极化SAR图像分类[J]. 清华大学学报(自然科学版), 2016, 56(8): 908-912.
[3] 韩赞东, 李永杰, 李晓阳. 残余奥氏体含量涡流检测仿真与特征提取[J]. 清华大学学报(自然科学版), 2016, 56(6): 617-621.
[4] 卢兆麟, 李升波, 徐少兵, 成波. 基于眼动跟踪特征的汽车造型评价方法[J]. 清华大学学报(自然科学版), 2015, 55(7): 775-781.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn