改进局部灰度特征的工件亚像素级边缘检测
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摘要
针对局部灰度特征方法在高噪声图像中定位精度低的问题,提出改进局部灰度特征方法并用于工件亚像素边缘检测。首先,使用改进的Canny算子代替基于一阶导数的梯度算子,以便更精确地提取粗边缘;然后在采集到的像素窗口两侧建立子图像,代替单个窗口特征以修改边缘上下两侧强度值,再将新构造的子图像中间列像素进行加权,加速图像迭代复原速度;最后,通过所有新生成的子图像局部灰度特征重新计算亚像素位置,进一步提高检测精度。实验结果表明,在无外加噪声和加入1.00%、5.00%的噪声标准偏差图像中,RMS误差明显降低,亚像素坐标数与定位精度明显提高。
In order to solve the problem of low localization accuracy of local grayscale feature method in high noise image,an improved local grayscale feature method is proposed and applied to the sub-pixel edge detection of workpiece. First of all,using the improved canny operator instead of the traditional derivative mask to extract rough edge more precisely,and then collected the pixel window on both sides to establish the image instead of a single window feature to modify the edge strength value on both sides. Then,the intermediate column pixels of the newly constructed subimage are weighted to speed up the iterative recovery of the image. Finally,the detection accuracy is further improved by recalculating the sub-pixel position through all newly generated sub-image features. The experimental results show that the RMS error is significantly reduced in the absence of additional noise and the addition of 1.00% and 5.00% noise standard deviation images.
In order to solve the problem of low localization accuracy of local grayscale feature method in high noise image,an improved local grayscale feature method is proposed and applied to the sub-pixel edge detection of workpiece. First of all,using the improved canny operator instead of the traditional derivative mask to extract rough edge more precisely,and then collected the pixel window on both sides to establish the image instead of a single window feature to modify the edge strength value on both sides. Then,the intermediate column pixels of the newly constructed subimage are weighted to speed up the iterative recovery of the image. Finally,the detection accuracy is further improved by recalculating the sub-pixel position through all newly generated sub-image features. The experimental results show that the RMS error is significantly reduced in the absence of additional noise and the addition of 1.00% and 5.00% noise standard deviation images.
引文
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[4]BIN T J,AO L,CUI J W,et al.Subpixel edge location based on orthogonal Fourier-Mellin moments[J].Image&Vision Computing,2008,26(4):563-569.
[5]QU Y D,CUI C S,CHEN S B,et al.A fast subpixel edge detection method using Sobel-Zernike moments operator[J].Image and Vision Computing,2005,23(1):11-17.
[6]HU Z,DANG H,LI X.A novel fast subpixel edge location method based on Sobel-OFMM[C].IEEE International Conference on Automation and Logistics,2008:828-832.
[7]苗青,李仰军,王高,等.基于改进的Sobel算子和Zernike矩的亚像素边缘检测[J].山西电子技术,2016(1):5-6.
[8]李庆利,张少军,李忠富,等.一种基于多项式插值改进的亚像素细分算法[J].工程科学学报,2003,25(3):280-283.
[9]BOUCHARA F,RAMDANI S.Subpixel edge refinement using deformable models[J].Journal of the Optical Society of America A-Optics Image Science and Vision,2009,26(4):820.
[10]魏本征,赵志敏,华晋.基于改进形态学梯度和Zernike矩的亚像素边缘检测方法[J].仪器仪表学报,2010,31(4):838-844.
[11]张舞杰,李迪,叶峰.基于Sigmoid函数拟合的亚像素边缘检测方法[J].华南理工大学学报:自然科学版,2009,37(10):39-43.
[12]SUN Q,HOU Y,TAN Q.A subpixel edge detection method based on an arctangent edge model[J].Optik-International Journal for Light and Electron Optics,2016,127(14):5702-5710.
[13]WU K,LI W,GE Y,et al.A subpixel edge detection algorithm based on the combination of border following and gray moment[C].IEEEInternational Conference on Nano/molecular Medicine and Engineering,2017:34-38.
[14]TRUJILLO-PINO A,KRISSIAN K,ALEMáN-FLORES M,et al.Accurate subpixel edge location based on partial area effect[J].Image&Vision Computing,2013,31(1):72-90.
[15]宿晓玮.基于像元分解的标志点亚像素定位研究[D].徐州:中国矿业大学,2014.
[16]丁洪利,吕晓东,王敬涛,等.基于亚像素的布氏硬度测量图像处理算法[J].工程与试验,2014(2):15-18.
[17]周炼,安晨辉,候晶,等.圆弧金刚石砂轮三维几何形貌的在位检测和误差评价[J].光学精密工程,2017,25(12):3079-3088.
[18]艾泽潭,石庚辰,熊鑫,等.基于模糊梯度的微齿轮图像边缘检测算法[J].北京理工大学学报,2011,31(8),919-921.
[19]CANNY J F.A computational approach to edge detection[J].IEEETransactions on PAML,1986,8(6):679-698.
[20]LIU Z Y,CUI X T,LI X H.Algorithm for image edge detection based on DCT compressed domain[J].Computer Technology&Development,2008(4):111-113+117.
[21]ZHAO M,LIU H,et al.An improved Canny edge detection algorithm based on DCT[C].IEEE International Conference on Progress in Informatics&Computing,2015:234-237.