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工业检测图像灰度波动变换自适应阈值分割算法

魏巍 申铉京 千庆姬

魏巍, 申铉京, 千庆姬. 工业检测图像灰度波动变换自适应阈值分割算法. 自动化学报, 2011, 37(8): 944-953. doi: 10.3724/SP.J.1004.2011.00944
引用本文: 魏巍, 申铉京, 千庆姬. 工业检测图像灰度波动变换自适应阈值分割算法. 自动化学报, 2011, 37(8): 944-953. doi: 10.3724/SP.J.1004.2011.00944
WEI Wei, SHEN Xuan-Jing, QIAN Qing-Ji. An Adaptive Thresholding Algorithm Based on Grayscale WaveTransformation for Industrial Inspection Images. ACTA AUTOMATICA SINICA, 2011, 37(8): 944-953. doi: 10.3724/SP.J.1004.2011.00944
Citation: WEI Wei, SHEN Xuan-Jing, QIAN Qing-Ji. An Adaptive Thresholding Algorithm Based on Grayscale WaveTransformation for Industrial Inspection Images. ACTA AUTOMATICA SINICA, 2011, 37(8): 944-953. doi: 10.3724/SP.J.1004.2011.00944

工业检测图像灰度波动变换自适应阈值分割算法

doi: 10.3724/SP.J.1004.2011.00944
详细信息
    通讯作者:

    千庆姬 吉林大学物理学院副教授. 1983年在吉林工业大学获得学士学位.主要研究方向为多媒体技术、光电检测技术、无线传感器网络、应用光学和光谱分析. 本文通信作者.E-mail: qianqj@jlu.edu.

An Adaptive Thresholding Algorithm Based on Grayscale WaveTransformation for Industrial Inspection Images

  • 摘要: 工业检测图像经常受到不均光照的影响,对该类图像局部自适应分割算法比全局算法能产生更好的分割效果. 但局部算法中基于分块的算法对分块方法缺乏指导,而基于邻域的算法容易在背景或前景内部产生误分. 针对上述缺点,本文提出了一种多方向灰度波动变换的自适应阈值分割算法. 该算法先从多个方向依照灰度波动对图像进行转换,构造以多维向量为基础的灰度波动变换矩阵, 然后利用主成分分析法(Principal component analysis, PCA)将高维向量压缩至一维并生成变换图像,最后运用Otsu算法分割变换图像. 该算法无需分块,并且仅需波动幅度阈值和布尔型背景色两个参数. 实验结果表明,该算法能够有效减少不均光照对工业检测图像分割的影响, 与Niblack法、Sauvola法等几种局部算法相比,该法在分割效果上具有了明显的提升.
  • [1] Bernsen J. Dynamic thresholding of gray-level images. In: Proceedings of the 8th International Conference Pattern Recognition. Paris, France: IEEE, 1986. 1251-1255[2] Niblack W. An Introduction to Digital Image Processing. New Jersey: Prentice Hall, 1986. 115-116[3] Taxt T, Flynn P J, Jain A K. Segmentation of document images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(12): 1322-1329[4] Sauvola J, Pietikainen M. Adaptive document image binarization. Pattern Recognition, 2000, 33(2): 225-236[5] Kim I J. Multi-window binarization of camera image for document recognition. In: Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition. Washington D. C., USA: IEEE, 2004. 323-327[6] Huang Q M, Gao W, Cai W J. Thresholding technique with adaptive window selection for uneven lighting image. Pattern Recognition Letters, 2005, 26(6): 801-808[7] Tsai Y H. A new approach for image thresholding under uneven lighting conditions. In: Proceedings of the 6th IEEE/ACIS International Conference on Computer and Information Science. Melbourne, Australia: IEEE, 2007. 123-127[8] Chou C H, Lin W H, Chang F. A binarization method with learning-built rules for document images produced by cameras. Pattern Recognition, 2010, 43(4): 1518-1530[9] Moghaddam R F, Cheriet M. A multi-scale framework for adaptive binarization of degraded document images. Pattern Recognition, 2010, 43(6): 2186-2198[10] Chen Q, Sun Q S, Heng P A, Xia D S. A double-threshold image binarization method based on edge detector. Pattern Recognition, 2008, 41(4): 1254-1267[11] Otsu N. A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66[12] Wang Hai-Yang, Pan De-Lu, Xia De-Shen. A fast algorithm for two-dimensional Otsu adaptive threshold algorithm. Acta Automatica Sinica, 2007, 33(9): 968-971(汪海洋, 潘德炉, 夏德深. 二维Otsu自适应阈值选取算法的快速实现. 自动化学报, 2007, 33(9): 968-971)[13] Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 2004, 13(1): 146-168[14] Jolliffe I T. Principal Component Analysis (Second Edition). New York: Springer-Verlag, 2002. 11-76
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出版历程
  • 收稿日期:  2010-06-22
  • 修回日期:  2011-02-01
  • 刊出日期:  2011-08-20

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