基于边缘和水平集的复杂背景金属零件二维条码精确定位方法

摘要/Abstract

摘要：

摘要：针对由于背景复杂、光照不均等因素导致的复杂背景下金属零件上二维条码定位困难问题,提出一种基于边缘及水平集的复杂背景金属零件二维条码精确定位方法.首先，通过直线检测方法定位条码的L边；然后，采用一种改进的水平集图像分割方法对可能的条码区域进行图像分割,利用条码的边缘轮廓特征、灰度及梯度直方图双峰特征进行二维条码区域判断；最后，通过区域Hough变换精确定位二维条码区域.实验结果表明,所提出的方法平均定位准确率可达到95%,且其处理时间小于1 s,适用于对大多数复杂背景下金属零件二维条码的定位.

关键词: 二维条码, 直线检测, 复杂背景, 水平集, 金属零件, 精确定位

Abstract:

Abstract： Aimed at the difficult location issue of twodimensional barcode marked in metal parts resulted from complex background and uneven illumination etc., this paper proposed an accurate localization method of 2D barcode marked in mental parts under complex background based on edge and levelset. First, the “L” edges of 2D barcode was located by line detection. Next, an improved levelset image segmentation was proposed to segment the probable barcode area, while the contour feature, gray and gradient histogram doublet features were used to judge if the probable barcode area had barcode or not. Finally, the 2D barcode area was accuratly located using Hough transform. The experimental result shows that the accuracy of the proposed method can reach up to 95%, and the processing time is less than 1 s, indicating that the proposed method is promising.

Key words: 2dimensional (2D) barcode, line detection, complex background, levelset, mental part, accurate localization

中图分类号:

TP 391.41

郭改放,何卫平,李夏霜,王健,吴振芳. 基于边缘和水平集的复杂背景金属零件二维条码精确定位方法[J]. 上海交通大学学报（自然版）, 2017, 51(1): 90-.

GUO Gaifang,HE Weiping,LI Xiashuang,WANG Jian,WU Zhenfang. Accurate Localization of 2D Barcode Marked in Mental Parts Under Complex Background Based on Edge and LevelSet[J]. Journal of Shanghai Jiaotong University, 2017, 51(1): 90-.

参考文献

［1］International Organization for Standardization. ISO/IEC 160222006: Information Technology Automatic Identification and Data Capture TechniquesData Matrix Bar Code Symbology Specification ［S］. Geneva: International Organization for Standardization, 2006.
［2］AMOULD S, AWCOCK G J, THOMAS R. Remote barcode localisation using mathematical morphology［C］∥International Conference on Image Processing and ITS Applications. UK: IET,   1999：642-646.
［3］VIARDGAUDIN C, NORMAND N, BARBA D. A bar code location algorithm using a twodimensional approach［C］∥International Conference on Document Analysis and Recognition.   Tsukuba, Japan: 1993:45-48.
［4］刘宁钟, 杨静宇. 基于波形分析的二维条码识别［J］. 计算机研究与发展, 2004, 41(3): 463-469.
LIU Ningzhong, YANG Jingyu. Recognition of twodimensional bar code based on waveform analysis［J］. Journal of Conputer Research and Development, 2004, 41(3): 463-469.
［5］JAIN A K, CHEN Y. Bar code localization using texture analysis［C］∥International Conference on Document Analysis and Recognition.    Tsukuba, Japan: IEEE, 1993:41-44.
［6］郝云峰,戚飞虎,蒋人杰. 一种新的基于机器学习的2维条形码检测算法［J］. 中国图象图形学报,2007,12(10):1873-1876.
HAO Yunfeng, QI Feihu, JIANG Renjie. A novel machine learning based algorithm to detect data matrix［J］. Journal of Image and Graphics, 2007,12(10):1873-1876.
［7］王伟, 何卫平, 雷蕾, 等. 复杂金属背景下二维条码区域的快速定位［J］. 天津大学学报(自然科学与工程技术版), 2013, 46(6): 531-538.
WANG Wei, HE Weiping, LEI Lei, et al. Speedy location of 2D barcode region under complicated metal background［J］. Journal of Tianjin University(Science and Technology), 2013, 46(6): 531-538.
［8］HUANG Q, CHEN W S, HUANG X A, et al. Data matrix code location based on finder pattern detection and bar code border fitting ［J］. Mathematical Problems in Engingeering, 2012(2):199-210.
［9］BURNS J B, HANSON A R, RISEMAN E M. Extracting straight lines［J］. IEEE Transaction Pattern Analysis and Machine Intelligence，1986，8（4）: 425-455.
［10］GIOI R G V, JAKUBOWICZ J, MOREL J M, et al. LSD: A fast line segment detector with a false detection control［J］. IEEE Transaction Pattern Analysis and Machine Intelligence， 2010，32(4):722-732.
［11］DESOLNEUX A, LADJAL S, MOISAN L, et al. Dequantizing image orientation［J］. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2002，11(10)：1129-1140.
［12］OSHER S, SETHIAN J A. Fronts propagating with curvature dependent speed: Algorithms based on the HamiltonJacobi formulation ［J］. Journal of Computational Physics, 1988, 79: 12-49.
［13］KASS M, WITKIN A, TERZOPOULOS D. Snakes: Active contour models ［J］. Intemation Journal of Computer Vision,1988,1(4):321-331.
［14］CHAN T F, VESE L A. Active contours without edges ［J］. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2001, 10(2): 266-277.
［15］LI C, KAO C Y, GORE J C, et al. Minimization of regionscalable fitting energy for image segmentation ［J］. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2008, 10(17):1940-1949.
［16］VOVK U, PERNUS F, LIKAR B, et al. A review of methods for correction of intensity inhomogeneity in MRI ［J］. IEEE Transactions on Medical Imaging, 2007, 26（3）：405-426.
［17］LI C M, XU C, GUI C, et al. Level set evolution without reinitialization: A new variational formulation［J］. IEEE Computer Society Conference on Computer Vision & Pattern Recognition, 2010,1：430-436.
［18］HA J E. A new method for detecting data matrix under similarity transform for machine vision applications ［J］. International Journal of Control Automation and Systems, 2011, 9(4): 737-741.

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