吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 528-534.doi: 10.13229/j.cnki.jdxbgxb201602030

• Orginal Article • Previous Articles     Next Articles

Fast recursive multi-thresholding algorithm

SHEN Xuan-jing1, 2, ZHANG He1, 2, CHEN Hai-peng1, 2, WANG Yu1, 2, 3   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
    3.College of Applied Technology,Jilin University, Changchun 130012, China
  • Received:2014-07-20 Online:2016-02-20 Published:2016-02-20

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Abstract: The Neighborhood Valley-emphasis method can not get the right threshold value in some cases, such as the valley feature between the target and background is not very distinct. In order to solve this problem, a global thresholding method is proposed. This method is based on the gray information around the valley-point neighborhood and the relative characteristics between the valley point and its adjacent crest-point. The proposed method weights the objective function with the gray information around the valley-point neighborhood and the relation between the valley-point and its adjacent crest-point. It improves the accuracy of the threshold obtained by OTSU. The optimal threshold got by the proposed method has less valley-to-crest ratio. In other word, the valley gray is taken as the optimal threshold, which has larger height difference with it adjacent crest-point. In order to improve the efficiency, a recursive single threshold method based on the aforesaid algorithm is used to achieve the image multi-threshold segmentation. Experiment results show that the proposed method has great segmentation performance and low time complexity.

Key words: computer application, image segmentation, multilevel thresholding, recursion, OTSU method, valley point

CLC Number: 

  • TP391
[1] Sahoo P K, Soltani S, Wong A K C. A survey of thresholding techniques[J]. Computer Vision, Graphics, and Image Processing, 1988, 41(2): 233-260.
[2] Sezgin M. Survey over image thresholding techniques and quantitative performance evaluation[J]. Journal of Electronic Imaging, 2004, 13(1): 146-168.
[3] Xue J H, Zhang Y J. Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for image thresholding[J]. Pattern Recognition Letters, 2012, 33(6): 793-797.
[4] Yimit A, Hagihara Y, Miyoshi T, et al. 2-D direction histogram based entropic thresholding[J]. Neurocomputing, 2013, 120: 287-297.
[5] 魏巍, 申铉京, 千庆姬, 等. 三维最大Renyi熵的灰度图像阈值分割算法[J]. 吉林大学学报: 工学版, 2011, 41(4): 1083-1088.
Wei Wei, Shen Xuan-jing, Qian Qing-ji, et al. Thresholding algorithm based on threedimensional Renyi's entropy[J]. Journal of Jilin University(Engineering and Technology Edition), 2011, 41(4): 1083-1088.
[6] 巢渊, 戴敏, 陈恺, 等. 基于广义反向粒子群与引力搜索混合算法的多阈值图像分割[J]. 光学精密工程, 2015, 23(3): 879-886.
He Zhi-yong, Sun Li-ning, Huang Wei-guo, et al.Thresholding segmentation algorithm based on Otsu criterion and line intercept histogram[J]. Optics and Precision Engineering, 2012, 20(10):2315-2323.
[7] 陈恺, 陈芳, 戴敏,等. 基于萤火虫算法的二维熵多阈值快速图像分割[J]. 光学精密工程, 2014, 22(2):517-523.
Chao Yuan, Dai Min, Chen Kai, et al. Image segmentation of multilevel threshold using hybrid PSOGSA with generalized opposition-based learning[J]. Optics and Precision Engineering, 2014, 22(2):517-523.
[8] Otsu N. A threshold selection method from gray-level histograms[J]. Automatica, 1975, 11(285-296): 23-27.
[9] Xu X, Xu S, Jin L, et al. Characteristic analysis of Otsu threshold and its applications[J]. Pattern Recognition Letters, 2011, 32(7): 956-961.
[10] Morii F. An image thresholding method using a minimum weighted squareddistortion criterion[J]. Pattern Recognition, 1994, 28(7): 1063-1071.
[11] Hou Z, Hu Q, Nowinski W L. On minimum variance thresholding[J]. Pattern Recognition Letters, 2006, 27(14): 1732-1743.
[12] Ng H F. Automatic thresholding for defect detection[J]. Pattern Recognition Letters, 2006, 27(14): 1644-1649.
[13] 申铉京, 龙建武, 陈海鹏, 等. 三维直方图重建和降维的Otsu阈值分割算法[J]. 电子学报, 2011, 39(5): 1108-1114.
Shen Xuan-jing, Long Jian-wu, Chen Hai-peng, et al. Otsu thresholding algorithm based on rebuilding and dimension reduction of the 3-dimensional histogram[J]. Acta Electronica Sinica, 2011, 39(5): 1108-1114.
[14] Fan J L, Lei B. A modified valley-emphasis method for automatic thresholding[J]. Pattern Recognition Letters, 2012, 33(6): 703-708.
[15] 张健, 李宏升. 基于图论阈值算法的图像分割研究[J]. 液晶与显示, 2014, 29(4):592-597.
Zhang Jian, Li Hong-shengl. Image mosaic research based on wavelet and rough set algorithm[J]. Chinese Journal of Liquid Crystal and Displays , 2014, 29(4):592-597.
[16] Berkley Image Database[EB/OL].[2013-11-12]. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/seg bench/.
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