Research on detection and location of weak edge signals
In order to be able to control the growth rate of industrial polycrystalline silicon rods well and reduce energy consumption, it is necessary to accurately locate the boundaries of the silicon rods and measure the diameter of the silicon rod during growth. In this paper, a weak edge signal detection...
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Published in | Signal, image and video processing Vol. 14; no. 7; pp. 1355 - 1360 |
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Main Authors | , , , |
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Language | English |
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Springer London
01.10.2020
Springer Nature B.V |
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Abstract | In order to be able to control the growth rate of industrial polycrystalline silicon rods well and reduce energy consumption, it is necessary to accurately locate the boundaries of the silicon rods and measure the diameter of the silicon rod during growth. In this paper, a weak edge signal detection method based on small area is proposed for the problem of weak boundary signal in the late growth stage of polycrystalline silicon rods. The method increases the gradient of the edge by projecting the boundary signal in the column direction. In addition, the method further enhances the gradient information of the weak boundary signal by improving the classical difference operator. In the experimental part, the improved difference operator increases the weak difference signal value from 304 to 686. Finally, the effectiveness of the algorithm is proved by two groups of experimental results. |
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AbstractList | In order to be able to control the growth rate of industrial polycrystalline silicon rods well and reduce energy consumption, it is necessary to accurately locate the boundaries of the silicon rods and measure the diameter of the silicon rod during growth. In this paper, a weak edge signal detection method based on small area is proposed for the problem of weak boundary signal in the late growth stage of polycrystalline silicon rods. The method increases the gradient of the edge by projecting the boundary signal in the column direction. In addition, the method further enhances the gradient information of the weak boundary signal by improving the classical difference operator. In the experimental part, the improved difference operator increases the weak difference signal value from 304 to 686. Finally, the effectiveness of the algorithm is proved by two groups of experimental results. |
Author | Xiaohan, Cao Zhen, Liu Xiuyun, Zhou Ting, Zhou |
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Keywords | Data processing Weak signal Edge signal processing Difference operator Edge detection |
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References_xml | – reference: WuHLiuXChenXPangJLiZXiongDSilicon rod diameter measurement method based on binocular vision techniqueJ. Atmosp. Environ. Opt.201495401408 – reference: JiangHCaoZLiuSDiameter detection and deposition process of CVD polysilicon reactorMod. Chem. Ind.2015353169172 – reference: Deng R, Shen C, Liu S, et al. Learning to predict crisp boundaries. (2018). https://doi.org/10.1007/978-3-030-01231-1_35 – reference: LiuXResearch on on-line monitoring system of silicon rod diameter in polycrystalline silicon reduction furnace2014HefeiAnhui University – reference: Pei S., Lee, T.: Effective image haze removal using dark channel prior and post-processing. In: 2012 IEEE International Symposium on Circuits and Systems. 2777–2780 (2012) – reference: Tan, RT.: Visibility in bad weather from a single image. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008). 2347–2354 (2008) – reference: YoungNEvansANMedian centred difference gradient operator and its application in watershed segmentationElectron. Lett.201147317818010.1049/el.2010.3294 – reference: XieSTuZHolistically-nested edge detectionInt. J. Comput. Vis.20171251–3318371783310.1007/s11263-017-1004-z – reference: YangXLvYResearch and implementation of grayscale image threshold segmentation based on difference operators combined with improved otsu algorithmInstrum. Tech. Sens.20153104106 – reference: LiuYChengMMHuXRicher convolutional features for edge detectionIEEE Trans. Pattern Anal. Mach. Intell.20194181939194610.1109/TPAMI.2018.2878849 – reference: SinghDKumarVSingle image haze removal using integrated dark and bright channel priorMod. Phys. Lett. B2018376153510.1142/S0217984918500513 – reference: Narasimhan, S., Nayar, S.: Chromatic framework for vision in bad weather. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (CVPR2000), (2000). https://doi.org/10.1109/CVPR.2000.855874 – reference: SunXSunJZhaoLCaoYImproved algorithm for single image haze removing using dark channel priorJ. Image Gr.20143381385 – reference: NarasimhanSNayarKVision and the AtmosphereInt. J. Comput. Vis.200248323325410.1023/A:1016328200723 – reference: HeKSunJTangXSingle image haze removal using dark channel priorIEEE Trans. Pattern Anal. Mach. Intell.201133122341235310.1109/TPAMI.2010.168 – reference: BhardwajSMittalAA survey on various edge detector techniquesProc. Technol.2012422022610.1016/j.protcy.2012.05.033 – reference: FattalRSingle image dehazingACM Trans. Gr.200810.1145/1399504.1360671 – ident: 1679_CR15 doi: 10.1109/CVPR.2008.4587643 – ident: 1679_CR12 doi: 10.1109/CVPR.2000.855874 – ident: 1679_CR7 doi: 10.1007/978-3-030-01231-1_35 – year: 2018 ident: 1679_CR11 publication-title: Mod. Phys. Lett. B doi: 10.1142/S0217984918500513 – volume: 33 start-page: 2341 issue: 12 year: 2011 ident: 1679_CR8 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2010.168 – volume: 3 start-page: 104 year: 2015 ident: 1679_CR16 publication-title: Instrum. Tech. Sens. – volume: 47 start-page: 178 issue: 3 year: 2011 ident: 1679_CR17 publication-title: Electron. Lett. doi: 10.1049/el.2010.3294 – volume: 35 start-page: 169 issue: 3 year: 2015 ident: 1679_CR2 publication-title: Mod. Chem. Ind. – volume: 9 start-page: 401 issue: 5 year: 2014 ident: 1679_CR3 publication-title: J. Atmosp. Environ. Opt. – year: 2008 ident: 1679_CR14 publication-title: ACM Trans. Gr. doi: 10.1145/1399504.1360671 – volume: 3 start-page: 381 year: 2014 ident: 1679_CR9 publication-title: J. Image Gr. – volume: 125 start-page: 3 issue: 1–3 year: 2017 ident: 1679_CR5 publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-017-1004-z – volume: 48 start-page: 233 issue: 3 year: 2002 ident: 1679_CR13 publication-title: Int. J. Comput. Vis. doi: 10.1023/A:1016328200723 – volume: 4 start-page: 220 year: 2012 ident: 1679_CR4 publication-title: Proc. Technol. doi: 10.1016/j.protcy.2012.05.033 – volume: 41 start-page: 1939 issue: 8 year: 2019 ident: 1679_CR6 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2018.2878849 – ident: 1679_CR10 doi: 10.1109/ISCAS.2012.6271886 – volume-title: Research on on-line monitoring system of silicon rod diameter in polycrystalline silicon reduction furnace year: 2014 ident: 1679_CR1 |
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SubjectTerms | Algorithms Computer Imaging Computer Science Diameters Energy consumption Finite differences Image Processing and Computer Vision Multimedia Information Systems Operators (mathematics) Original Paper Pattern Recognition and Graphics Polycrystals Polysilicon Rods Signal detection Signal,Image and Speech Processing Silicon Stability Vision |
Title | Research on detection and location of weak edge signals |
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