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.Abstract: The industrial inspection images are usually under non-uniform illumination, and local adaptive thresholding algorithms have better segmentation performance on them than the global ones. But the local algorithms based on image's sub-blocks are short of instructions for partitioning, and the local algorithms based on pixel's neighborhood will probably cause some misclassifications within the background or foreground. To resolve these problems, a novel adaptive thresholding algorithm based on multi-directional grayscale wave transformation is proposed in this paper. Firstly, it performs the transformation by grayscale waves in multi-directions to get a matrix of multi-dimensional vectors. Secondly, the vectors are compressed to one dimension using the principal component analysis (PCA) method, and then the Otsu global method is employed to find optimal wave threshold for segmentation on this matrix. This algorithm does not need partitioning the image any more and only takes the peak height threshold and the boolean background color as its two parameters. Experiments demonstrate that this method has a excellent capability of decreasing the influence of non-uniform illumination in industrial inspection images, and its segmentation performance is better than several other local thresholding algorithms, such as Niblack's method and Sauvola's method.
-
Key words:
- Image segmentation /
- local thresholding segmentation /
- grayscale wave /
- Otsu method
-
[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
点击查看大图
计量
- 文章访问数: 2485
- HTML全文浏览量: 77
- PDF下载量: 1304
- 被引次数: 0