An improved binarization algorithm of wood image defect segmentation based on non-uniform background
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摘要
In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of nonuniform backgrounds of wood defect images.The proposed algorithm calculates the threshold by the mean,standard deviation and the extreme value of the window.The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background,which is much superior to the global threshold algorithm and the Bernsen algorithm,and slightly better than the Niblack algorithm and Sauvola algorithm.Compared with similar models,the algorithm proposed in this paper has higher segmentation accuracy,as high as 92.6% for wood defect images with a complex background.
In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of nonuniform backgrounds of wood defect images.The proposed algorithm calculates the threshold by the mean,standard deviation and the extreme value of the window.The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background,which is much superior to the global threshold algorithm and the Bernsen algorithm,and slightly better than the Niblack algorithm and Sauvola algorithm.Compared with similar models,the algorithm proposed in this paper has higher segmentation accuracy,as high as 92.6% for wood defect images with a complex background.
In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of nonuniform backgrounds of wood defect images.The proposed algorithm calculates the threshold by the mean,standard deviation and the extreme value of the window.The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background,which is much superior to the global threshold algorithm and the Bernsen algorithm,and slightly better than the Niblack algorithm and Sauvola algorithm.Compared with similar models,the algorithm proposed in this paper has higher segmentation accuracy,as high as 92.6% for wood defect images with a complex background.
引文
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Dong W,Li H,Wei X,Wang X(2017)An efficient iterative thresholding method for image segmentation.J Comput Phy350:657-667
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Hu R,Odobez JM,Gatica-Perez D(2017)Extracting Maya Glyphs from degraded ancient documents via image segmentation.J Comput Cult Herit(JOCCH)10(2):10
Jin HE,Liu TG,Zhang H,Zhang ZC(2009)Adaptive algorithm based on morphological top-hat transformation to segment Chinese square seals in bank checks.Opt Precis Eng17(10):2576-2585
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Liu S,Zhang L,Zhang Z,Wang C,Xiao B(2015)Automatic cloud detection for all-sky images using superpixel segmentation.IEEE Geosci Remote Sens Lett 12(2):354-358
Lu R,Dong Y(2016)Component surface defect detection based on image segmentation method.In:Control decision conference
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Mostafa A,Elfattah MA,Fouad A,Hassanien AE,Hefny H(2016)Wolf local thresholding approach for liver image segmentation in CT images.In:Proceedings of the second international AfroEuropean conference for industrial advancement AECIA 2015
Najafi MH,Salehi ME(2016)A fast fault-tolerant architecture for Sauvola local image thresholding algorithm using stochastic computing.IEEE Trans Very Large Scale Integr Syst24(2):808-812
Otsu N(2007)A threshold selection method from gray-level histograms.IEEE Trans Syst Man Cybern 9(1):62-66
Ouyang Q(2006)Uneven illumination license plate image binarization.J Wuhan Univ:Eng Ed 39(4):143-146
Samorodova OA,Samorodov AV(2016)Fast implementation of the niblack binarization algorithm for microscope image segmentation.Pattern Recognit Image Anal 26(3):548-551
Senthilkumaran N,Vaithegi S(2016)Image segmentation by using thresholding techniques for medical images.Comput Sci Eng6(1):1-13
Shu A,Ruan Q(2017)3D facial expression recognition algorithm using local threshold binary pattern and histogram of oriented gradient.In:IEEE international conference on signal processing
Vala MHJ,Baxi A(2013)A review on Otsu image segmentation algorithm.Int J Adv Res Comput Eng Technol(IJARCET)2(2):387
Vijayan G,Reshma SR,Dhanya FE,Anju S,Nair GR,Aneesh RP(2017)A novel shadow removal algorithm using Niblack segmentation in satellite images.In:International conference on communication systems&networks
Vo GD,Park C(2018)Robust regression for image binarization under heavy noise and nonuniform background.Pattern Recognit81:224-239
Yi W,Wang J,Sun X,Ming H(2010)A modified Otsu image segment method based on the Rayleigh distribution.In:IEEEinternational conference on computer science&information technology
Yu L(2012)Study on automatic threshold selection algorithm of sensor images.Phys Procedia 25:1769-1775
Zhang Y(2001)License plate binarization algorithm based on analysis of the spatial distribution and maximum variance between clusters.J Zhejiang Univ 2001(03):42-45?50
Zheng X,Wei T,Du J(2011)A fast adaptive binarization method based on sub block OSTU and improved sauvola.In:International conference on wireless communications
Cetiner I,Var AA,Cetiner H(2014)Wood surface analysis with image processing techniques.In:Signal processing&communications applications conference
Dalida JPD,Galiza AJN,Godoy AGO,Nakaegawa MQ,Vallester JLM,Cruz ARD(2017)Development of intelligent transportation system for Philippine license plate recognition.In:IEEERegion 10 conference
Dong W,Li H,Wei X,Wang X(2017)An efficient iterative thresholding method for image segmentation.J Comput Phy350:657-667
Elyounsi A,Tlijani H,Bouhlel MS(2017)Combining top-hat,thresholding and watershed transformation for 3D inverse synthetic aperture radar images segmentation.In:International conference on sciences of electronics
Gu IYH,Andersson H,Vicen R(2008)Automatic classification of wood defects using support vector machines.In:Bolc L,Kulikowski JL,Wojciechowski K(eds)Computer vision and graphics.ICCVG 2008.Lecture notes in computer science,vol5337.Springer,Berlin,pp 356-367
Guo J,Liu XY,Wu B,Fu XW(2014)A binarization method for uneven illumination images.Comput Appl Softw31(03):183-186
Hittawe MM,Muddamsetty SM,Sidibe D,Meriaudeau F(2015)Multiple features extraction for timber defects detection and classification using SVM.In:IEEE international conference on image processing
Hu R,Odobez JM,Gatica-Perez D(2017)Extracting Maya Glyphs from degraded ancient documents via image segmentation.J Comput Cult Herit(JOCCH)10(2):10
Jin HE,Liu TG,Zhang H,Zhang ZC(2009)Adaptive algorithm based on morphological top-hat transformation to segment Chinese square seals in bank checks.Opt Precis Eng17(10):2576-2585
Liu,S(2013)Research for binarization of uneven illumination text image.In:Proceedings of SPIE-The international society for optical engineering,vol 8768(2),87686O-87686O-5
Liu S,Zhang L,Zhang Z,Wang C,Xiao B(2015)Automatic cloud detection for all-sky images using superpixel segmentation.IEEE Geosci Remote Sens Lett 12(2):354-358
Lu R,Dong Y(2016)Component surface defect detection based on image segmentation method.In:Control decision conference
Ma WK,Wang L,He H(2009)Local threshold segmentation algorithm for fingerprint images.Comput Eng Appl45(34):177-179
Mostafa A,Elfattah MA,Fouad A,Hassanien AE,Hefny H(2016)Wolf local thresholding approach for liver image segmentation in CT images.In:Proceedings of the second international AfroEuropean conference for industrial advancement AECIA 2015
Najafi MH,Salehi ME(2016)A fast fault-tolerant architecture for Sauvola local image thresholding algorithm using stochastic computing.IEEE Trans Very Large Scale Integr Syst24(2):808-812
Otsu N(2007)A threshold selection method from gray-level histograms.IEEE Trans Syst Man Cybern 9(1):62-66
Ouyang Q(2006)Uneven illumination license plate image binarization.J Wuhan Univ:Eng Ed 39(4):143-146
Samorodova OA,Samorodov AV(2016)Fast implementation of the niblack binarization algorithm for microscope image segmentation.Pattern Recognit Image Anal 26(3):548-551
Senthilkumaran N,Vaithegi S(2016)Image segmentation by using thresholding techniques for medical images.Comput Sci Eng6(1):1-13
Shu A,Ruan Q(2017)3D facial expression recognition algorithm using local threshold binary pattern and histogram of oriented gradient.In:IEEE international conference on signal processing
Vala MHJ,Baxi A(2013)A review on Otsu image segmentation algorithm.Int J Adv Res Comput Eng Technol(IJARCET)2(2):387
Vijayan G,Reshma SR,Dhanya FE,Anju S,Nair GR,Aneesh RP(2017)A novel shadow removal algorithm using Niblack segmentation in satellite images.In:International conference on communication systems&networks
Vo GD,Park C(2018)Robust regression for image binarization under heavy noise and nonuniform background.Pattern Recognit81:224-239
Yi W,Wang J,Sun X,Ming H(2010)A modified Otsu image segment method based on the Rayleigh distribution.In:IEEEinternational conference on computer science&information technology
Yu L(2012)Study on automatic threshold selection algorithm of sensor images.Phys Procedia 25:1769-1775
Zhang Y(2001)License plate binarization algorithm based on analysis of the spatial distribution and maximum variance between clusters.J Zhejiang Univ 2001(03):42-45?50
Zheng X,Wei T,Du J(2011)A fast adaptive binarization method based on sub block OSTU and improved sauvola.In:International conference on wireless communications