基于HSI颜色空间与灰度波动相结合的复杂桥梁蜂窝麻面的图像分割
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摘要
针对桥梁蜂窝麻面图像经常存在光照不均、多背景并存的干扰问题,提出了基于HSI颜色空间与灰度波动相结合的复杂桥梁蜂窝麻面的图像分割算法。首先,绘制S分量灰度变化曲线;其次,搜索曲线所有潜在的波峰波谷,并求相邻波峰波谷的高度差;然后,基于灰度像素个数差分值的标准差筛选出部分高度差;最后,基于部分高度差的标准差搜索最佳阈值完成图像的阈值分割。实验结果表明,与二维OTSU法、Niblack法、二维Tsallis熵法等几种算法相比,该算法的分割效果和实时性更好。
As the voids and pits image of bridge often has uneven illumination and multi-background interference problems, an image segmentation algorithm for complex voids and pits of bridge was proposed based on HSI color space and gray fluctuation. Firstly, S-component gray curve was plotted and all the potential peaks and troughs of the curve were searched, then the height differences between adjacent peaks and thoughs were calculated. Secondly, partial height differences were selected based on the standard deviation of gray pixel difference value. Finally the threshold segmentation of image was finished by searching the best threshold based on the standard deviation of partial height differences. Experimental results show that the proposed algorithm has better segmentation effect and real-time performance than OTSU, Niblack and Tsallis entropy method.
As the voids and pits image of bridge often has uneven illumination and multi-background interference problems, an image segmentation algorithm for complex voids and pits of bridge was proposed based on HSI color space and gray fluctuation. Firstly, S-component gray curve was plotted and all the potential peaks and troughs of the curve were searched, then the height differences between adjacent peaks and thoughs were calculated. Secondly, partial height differences were selected based on the standard deviation of gray pixel difference value. Finally the threshold segmentation of image was finished by searching the best threshold based on the standard deviation of partial height differences. Experimental results show that the proposed algorithm has better segmentation effect and real-time performance than OTSU, Niblack and Tsallis entropy method.
引文
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[21]JOLLIFFE I T.Principal component analysis[J].Journal of Marketing Research,2002,87(100):513.
[2]ZHENG X,YE H,TANG Y.Image bi-level thresholding based on gray level-local variance histogram[J].Entropy,2017,19(5):191.
[3]OSTU N.A threshold selection method from gray-level histogram[J].IEEE Transactions on Systems,Man,and Cybernetics,1978,9(1):62-66.
[4]de ALBUQUERQUE M P,ESQUEF I A,MELLO A R G,et al.Image thresholding using Tsallis entropy[J].Pattern Recognition Letters,2004,25(9):1059-1065.
[5]MANIKANTAN K,ARUN B V,YARADONI D K S.Optimal multilevel thresholds based on tsallis entropy method using golden ratio particle swarm optimization for improved image segmentation[J].Procedia Engineering,2012,30:364-371.
[6]ALVA A,AKASH R S,MANIKANTAN K.Optimal multilevel thresholding based on Tsallis entropy and half-life constant PSO for improved image segmentation[C]//Proceedings of the 2015 IEEEUP Section Conference on Electrical Computer and Electronics.Piscataway,NJ:IEEE,2016:1-6.
[7]WANG G-Q,ZHOU X-H,YU L-L.Image segmentation based on watershed algorithm[J].Computer Simulation,2009,26(5):255-258.
[8]YAHYA A A,TAN J,HU M.A novel model of image segmentation based on watershed algorithm[J].Advances in Multimedia,2013,2013:Article No.5.
[9]HETTIARACHCHI R,PETERS J F.Voronoi region-based adaptive unsupervised color image segmentation[J].Pattern Recognition,2016,65:119-135.
[10]ARIFIN A Z,ASANO A.Image segmentation by histogram thresholding using hierarchical cluster analysis[J].Pattern Recognition Letters,2006,27(13):1515-1521.
[11]NIBLACK W.An Introduction to Digital Image Processing[M].Birkeroed:Strandberg,1985:S106-S107.
[12]WANG X W,WU Q X,ZHANG Z,et al.Segmentation based on spiking neural network using color edge gradient for extraction of corridor floor[C]//Proceedings of the 3rd International Conference on Multimedia Technology,LNEE 278.Berlin:Springer,2013:275-285.
[13]LIN X,WANG X,CUI W.An automatic image segmentation algorithm based on spiking neural network model[C]//Proceedings of the 2014 International Conference on Intelligent Computing,LNCS8588.Berlin:Springer,2014:248-258.
[14]ZUO L,MA L,XIAO Y,et al.A dynamic region generation algorithm for image segmentation based on spiking neural network[C]//Proceedings of the 2017 International Conference on Neural Information Processing,LNCS 10636.Berlin:Springer,2017:816-824.
[15]朱磊,白瑞林,吉峰.光照不均匀图像的灰度波动局部阈值分割[J].计算机工程与应用,2015,51(12):144-149.(ZHU L,BAIR L,JI F.Local threshold segmentation based on grayscale wave for uneven illumination image[J].Computer Engineering and Applications,2015,51(12):144-149.)
[16]VALIZADEH M,KABIR E.An adaptive water flow model for binarization of degraded document images[J].International Journal on Document Analysis and Recognition,2013,16(2):165-176.
[17]汪鼎文,李元香.基于HSI颜色空间的X射线彩色图像分割[J].计算机工程与设计,2006,27(1):139-141.(WANG D W,LI Y X.X-ray color image segmentation based on HSI model[J].Computer Engineering and Design,2006,27(1):139-141.)
[18]GONZALEZ R C,WOODS R E,EDDINS S L,等.数字图像处理[M].北京:电子工业出版社,2014.(GONZALEZ R C,WOODS R E,EDDINS S L,et al.Digital Image Processing[M].Beijing:Publishing House of Electronics Industry,2014.)
[19]李峻,孟正大.基于HS分量联合统计的自适应阈值分割算法[J].东南大学学报(自然科学版),2010,40(S1):266-271.(LIJ,MENG Z D.HS joint statistics based adaptive color segmentation[J].Journal of Southeast University(Natural Science Edition),2010,40(S1):266-271.)
[20]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[J].自动化学报,2011(8):944-953.(WEI W,SHENX J,QIAN Q J.An adaptive thresholding algorithm based on grayscale transformation for industrial inspection images[J].Acta Automatica Sinica,2011(8):944-953.)
[21]JOLLIFFE I T.Principal component analysis[J].Journal of Marketing Research,2002,87(100):513.