Please wait a minute...
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)  2018, Vol. 52 Issue (5): 943-950    DOI: 10.3785/j.issn.1008-973X.2018.05.014
Mechanical and Energy Engineering     
Fast detection method for ellipse in complex background based on gradient grouping
WU Chen-rui, ZHANG Shu-you, HE Zai-xing
State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China
Download:   PDF(5273KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A novel ellipse detection method based on gradient clustering and convex polygon was proposed in order to solve the problem of slow detecting speed, low accuracy and high error rates of ellipse detection in complex background. Edge information including location and orientation was extracted through Canny operator in image preprocessing. Edges were clustered into four categories according to their gradient orientation and convexity. Unqualified ellipse tribes were quickly filtered according to convex polygon property. A non-iterative geometric least square method was used to fit the ellipse. Ellipses with small fitting errors were confirmed to be the final ellipse features in complex background. Experimental results show that the proposed method performs better than the classical algorithm in both recognition accuracy and calculating time.



Received: 15 April 2017      Published: 07 November 2018
CLC:  TP391.4  
Cite this article:

WU Chen-rui, ZHANG Shu-you, HE Zai-xing. Fast detection method for ellipse in complex background based on gradient grouping. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(5): 943-950.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2018.05.014     OR     http://www.zjujournals.com/eng/Y2018/V52/I5/943


基于梯度分类的复杂背景椭圆快速检测方法

针对复杂背景下椭圆特征由于重叠、缺失、嵌套等原因导致的检测效率低、误检率高的问题,提出基于梯度分类与多边形辨识的椭圆快速检测方法. 该方法通过边缘检测算子对采集的图像进行预处理,获取图像边缘的梯度信息. 根据边缘灰度梯度与凹凸性将边缘线分为4类圆弧特征,通过对4类圆弧特征的聚类初步确定备选的椭圆集合. 利用椭圆内包多边形为凸多边形的特点,对候选椭圆集合进行快速辨识. 应用非迭代几何最小二乘法拟合椭圆参数,通过椭圆残差判定与椭圆的去伪过程,获得最终的椭圆特征. 实验结果表明,该方法在椭圆检测效率与准确性上较经典算法均有提升.

[1] HUTTER M, BREWER N. Matching 2-D ellipses to 3-D circles with application to vehicle pose identification[C]//Image and Vision Computing New Zealand. Wellingtaon:IEEE, 2009:153-158.
[2] SOETEDJO A, YAMADA K. Fast and Robust Traffic Sign Detection[C]//IEEE International Conference on Systems, Man and Cybernetics. Waikoloa:IEEE, 2006:1341-1346.
[3] KIM H S, KANG W S, SHIN J I, et al. Face detection using template matching and ellipse fitting[J]. Ieice Transactions on Information & Systems, 2000, 83(11):2008-2011.
[4] FENG X, FANG C, DING X, et al. Iris localization with dual coarse-to-fine Strategy[C]//International Conference on Pattern Recognition. HongKong:IEEE, 2006:553-556.
[5] 廖苗,赵于前,曾业战,等.基于支持向量机和椭圆拟合的细胞图像自动分割[J].浙江大学学报:工学版.2017,51(4):722-728. LIAO Miao, ZHAO Yu-qian, ZENG Ye-zhan, et al. Automatic segmentation for cell images based on support vectot machine and ellipse fitting[J]. Journal of Zhejiang University:Engineering Science, 2017,51(4):722-728.
[6] FELZENSZWALB P F, HUTTENLOCHER D P. Pictorial structures for object recognition[J]. International Journal of Computer Vision, 2005, 61(1):55-79.
[7] MCLAUGHLIN R A. Randomized hough transform:improved ellipse detection with comparison[J]. Pattern Recognition Letters, 1998, 19(3/4):299-305.
[8] CHENG Z, LIU Y. Efficient technique for ellipse detection using restricted randomized Hough transform[C]//International Conference on Information Technology:Coding and Computing. Las Vegas:IEEE, 2004(2):714-718.
[9] LU W, TAN J. Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT)[J]. Pattern Recognition, 2008,41(4):1268-1279.
[10] 邹荣,赵稼宸,凌俊,等.基于Hough投票空间的椭圆图像特征亚像素提取方法[J].光学技术.2016,42(2):141-145. ZOU Rong, ZHAO Jia-chen, Lin Jun, et al. Sub-pixel ellipse feature extraction method based on Hough voting space[J]. Optical Technique, 2016,42(2):141-145.
[11] FITZGIBBON A, PILU M, FISHER R B. Direct least square fitting of ellipses[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2010, 21(5):476-480.
[12] LIU Z Y, QIAO H. Multiple ellipses detection in noisy environments:A hierarchical approach[J]. Pattern Recognition, 2009, 42(11):2421-2433.
[13] MAINI E S. Enhanced direct least square fitting of ellipses[J]. International Journal of Pattern Recognition & Artificial Intelligence, 2012, 20(6):939-954.
[14] PRASAD D K, LEUNG M K H, CHO S Y. Edge curvature and convexity based ellipse detection method[J]. Pattern Recognition, 2012, 45(9):3204-3221.
[15] 吴尧锋,王文,卢科青,等.边界聚类椭圆快速检测方法[J].浙江大学学报:工学版.2016,50(3):405-411. WU Xiao-feng, WANG Wen, LU Ke-qing, et al.Fast ellipse detection based on edge grouping[J]. Journal of Zhejiang University:Engineering Science, 2016, 50(3):405-411.
[16] CHEN S, XIA R, ZHAO J, et al. A hybrid method for ellipse detection in industrial images[J]. Pattern Recognition, 2017, 68:82-98.
[17] MEDINA-CARNICER R, OZ-SALINAS R, YEGUAS-BOLIVAR E, et al. A novel method to look for the hysteresis thresholds for the Canny edge detector[J]. Pattern Recognition, 2011, 44(6):1201-1211.
[18] CHATBRI H, KAMEYAMA K. Using scale space filtering to make thinning algorithms robust against noise in sketch images[J]. Pattern Recognition Letters. 2014, 42(6):1-10.
[19] PRASAD D K, LEUNG M K H, CHAI Q. ElliFit:An unconstrained, non-iterative, least squares based geometric Ellipse Fitting method[J]. Pattern Recognition, 2012, 46(5):1449-1465.
[20] AHN S J, RAUH W, WARNECKE H J, et al. Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola[J]. Pattern Recognition, 2001, 34(12):2283-2303.

[1] ZHENG Zhou, ZHANG Xue-chang, ZHENG Si-ming, SHI Yue-ding. Liver segmentation in CT images based on region-growing and unified level set method[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(12): 2382-2396.
[2] SUN Peng-peng, ZHAO Xiang-mo, XU Zhi-gang, MIN Hai-gen. Urban curb robust detection algorithm based on 3D-LIDAR[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(3): 504-514.
[3] LU Xiao-hui, WANG Jin, LU Guo-dong, ZHANG Dong-liang. Portrait skin beautification technology using multiple feature masks[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(12): 2299-2310.
[4] CA Jin-Hui, ZHANG Guang-Xin, CAI Hui. Theory and application of connectivity maskbased reconstruction
opening operator
[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2010, 44(4): 675-680.
[5] WANG Xuan-Yin, LIANG Dong-Tai. Surface defect detection based on multivariate image analysis[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2010, 44(3): 448-452.
[6] SUN Zhi-Hai, KONG Mo-Ceng, SHU Shan-An. Improved algorithm of subtractive clustering for object location  in video sequences[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2010, 44(3): 458-462.
[7] MAO Feng, ZHANG Shu-Wei, HUANG Chang-Lin. Inspection of foreign substances in mould using image scattergrams and multi|resolution analysis[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2009, 43(10): 1749-1756.
[8] FAN Xiang, XIA Shun-ren. Feature based automatic stitching of microscopic images[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2009, 43(7): 1182-1186.
[9] CHEN Cheng, PENG Huo-Ting, XIAO Dun. Video foreground segmentation with camera movement[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2009, 43(6): 975-977.
[10] XU Shu-Chang, ZHANG San-Yuan, ZHANG Yin. Estimation of skin pigment concentration by simulating skin color[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2009, 43(6): 983-986.
[11] ZHANG Dong-Mei, LIU Li-Gang. Angle-filtering based smoothing algorithm for planar graphs[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2009, 43(6): 1042-1046.
[12] CHEN Shuo, ZHANG Feng, CHEN Mo-Wei. Brightness perceptive user model based on simultaneous brightness contrast phenomena[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2009, 43(5): 822-826.
[13] ZHANG Zan-Chao, JIA Shun-Ren. Automatic urinary image processing[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2009, 43(5): 832-838.
[14] CHENG Bang-Qing, TANG Xiao-Wei. Study of Harris scale invariant keypoint detector[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2009, 43(5): 855-859.