The DOI matches its Crossref record; 32 of the 37 references it cites were found in a registry or an index.
Surface defect identification of aluminium strips with non-subsampled shearlet transform
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The shearlet transform sets up disparate direction amounts at diverse decomposition scales and is preeminent when approximating 2D smooth functions with discontinuities along C2-curves; it is fit to analyze images with complicated backgrounds and has been successfully applied to steel defects.
“Compared to other methods, it sets up disparate direction amounts at diverse decomposition scales and is preeminent when approximating 2D smooth functions with discontinuities along the C2-curves; the ST is fit to analyze images with complicated backgrounds and has been successfully applied to defecting steel defects in [101, 102].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [102]
Shearlet transform cannot effectively retain the detail information of the original images.
“Cannot effectively retain the detail information of the original images.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [102]
Shearlet transform has good sparsity.
“Good sparsity.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [102]
SVM is a generalized linear classifier for binary classification and has been widely used for defect classification of flat steel surfaces.
“SVM is a generalized linear classifier for binary classification, which has been widely used for the defect classification of flat steel surfaces [67, 100-102, 118].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [102]
Citations
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Locality Preserving Projections
He, X., & Niyogi, P. (2003) · Cited by 4,128
Fast Discrete Curvelet Transforms
Candès, E., Demanet, L., Donoho, D., & Ying, L. (2006) · Multiscale Modeling & Simulation · doi:10.1137/05064182x · Cited by 2,573
An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features
He, Y., Song, K., Meng, Q., & Yan, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2915404 · Cited by 1,341
Sparse directional image representations using the discrete shearlet transform
Easley, G. R., Demetrio Labate, A., & Lim, W. (2007) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2007.09.003 · Cited by 1,038
Song, K., & Yan, Y. (2013) · Applied Surface Science · doi:10.1016/j.apsusc.2013.09.002 · Cited by 1,026
Optimally Sparse Multidimensional Representation Using Shearlets
Guo, K., & Demetrio Labate, A. (2007) · SIAM Journal on Mathematical Analysis · doi:10.1137/060649781 · Cited by 687
Directional multiscale modeling of images using the contourlet transform
Po, D. D.-Y., & Do, M. N. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.873450 · Cited by 578
Rotation-invariant texture classification using feature distributions
Pietikäinen, M., Ojala, T., & Xu, Z. (2000) · Pattern Recognition · doi:10.1016/s0031-3203(99)00032-1 · Cited by 461
Sparse multidimensional representation using shearlets
Demetrio Labate, A., Lim, W., Gitta Kutyniok, B., & Weiss, G. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.613494 · Cited by 455
The Discrete Shearlet Transform: A New Directional Transform and Compactly Supported Shearlet Frames
Lim, W. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2041410 · Cited by 428
Surface defect classification of steels with a new semi-supervised learning method
Di, H., Ke, X., Peng, Z., & Dongdong, Z. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.01.011 · Cited by 241
Self-citationWavelets with composite dilations and their MRA properties
Guo, K., Demetrio Labate, A., Lim, W., Weiss, G., & Wilson, E. (2005) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2005.07.002 · Cited by 206
Xue-wu, Z., Yan-qiong, D., Yan-yun, L., Ai-ye, S., & Rui-yu, L. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.11.030 · Cited by 188
Novel fusion method for visible light and infrared images based on NSST–SF–PCNN
Kong, W., Zhang, L., & Yang, L. (2014) · Infrared Physics & Technology · doi:10.1016/j.infrared.2014.04.003 · Cited by 173
Image lightness rescaling using sigmoidal contrast enhancement functions
Braun, G. J., & Fairchild, M. D. (1999) · Journal of Electronic Imaging · doi:10.1117/1.482706 · Cited by 101
Supervised kernel locality preserving projections for face recognition
Cheng, J., Liu, Q., Lu, H., & Chen, Y. (2005) · Neurocomputing · doi:10.1016/j.neucom.2004.08.006 · Cited by 97
Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features
Ashour, M. W., Khalid, F., Abdul Halin, A., Abdullah, L. N., & Darwish, S. H. (2019) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-018-3329-5 · Cited by 77
Application of RNAMlet to surface defect identification of steels
Xu, K., Xu, Y., Zhou, P., & Wang, L. (2018) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2018.01.010 · Cited by 66
Self-citationRecent Advances in Mechatronics
Jabłoński, R., Turkowski, M., & Szewczyk, R. (2007) · Springer eBooks · doi:10.1007/978-3-540-73956-2 · Cited by 64
Defect detection for corner cracks in steel billets using a wavelet reconstruction method
Jeon, Y.-J., Choi, D.-C., Lee, S. J., Yun, J. P., & Kim, S. W. (2014) · Journal of the Optical Society of America A · doi:10.1364/josaa.31.000227 · Cited by 58
Application of Shearlet transform to classification of surface defects for metals
Xu, K., Liu, S., & Ai, Y. (2015) · Image and Vision Computing · doi:10.1016/j.imavis.2015.01.001 · Cited by 55
Self-citation(2000) · doi:10.1109/kes.2000.885769 · Cited by 45
CORD, A., BACH, F., & JEULIN, D. (2010) · Journal of Microscopy · doi:10.1111/j.1365-2818.2010.03365.x · Cited by 45
Unified detection method of aluminium profile surface defects: Common and rare defect categories
Zhang, D., Song, K., Xu, J., He, Y., & Yan, Y. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.105936 · Cited by 36
Feature extraction based on contourlet transform and its application to surface inspection of metals
Ai, Y., & Xu, K. (2012) · Optical Engineering · doi:10.1117/1.oe.51.11.113605 · Cited by 25
Self-citationWeiwei, L., Yunhui, Y., Jun, L., Yao, Z., & Hongwei, S. (2008) · 2008 Second International Symposium on Intelligent Information Technology Application · doi:10.1109/iita.2008.67 · Cited by 23
(2009) · 2009 ICCAS-SICE · Cited by 15
Wu, X.-Y., Xu, K., & Xu, J. (2010) · Acta Automatica Sinica · doi:10.3724/sp.j.1004.2010.00438 · Cited by 10
Self-citationImage lightness rescaling using sigmoidal contrast enhancement functions
Braun, G. J., & Fairchild, M. D. (1998) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.334548 · Cited by 7
Yin, M. (2014) · Jisuanji gongcheng · Cited by 1
Application of local binary patterns to surface defect recognition of continuous casting slabs
Si-yang, T., Ke, X., & Hui-zhao, G. (2016) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.13374/j.issn2095-9389.2016.12.010 · Cited by 1
Flagged first, then by citations
5 of the 37 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Caleb, 2000, Classification of surface defects on hot rolled steel using adaptive learning methods, KES’2000, 1, 103
- Qiang, 2007, Recognition of surface defects on medium and heavy plates based on structure spectrum, J Univ Sci Technol Beijing, 29, 342
- Hong, 2012, Visible and infrared image fusion algorithm based on shearlet transform, Chin J Sci Instrum, 33, 1613
- He, 2002, Locality preserving projections (LPP), Adv Neural Inf Process Syst, 16, 186
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Intelligent Manufacturing Technology in the Steel Industry of China: A Review
Zhou, D., Xu, K., Lv, Z., Yang, J., Li, M., He, F., & Xu, G. (2022) · Sensors · doi:10.3390/s22218194 · Cited by 82
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A detection method of spangle defects on zinc-coated steel surfaces based on improved YOLO-v5
Xiao, D., Xie, F. T., Gao, Y., Li, Z. N., & Xie, H. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11963-4 · Cited by 36
A non-subsampled Shearlet transform based approach for heartbeat sound watermarking
Sayah, M. M., Kafi, M. R., & Khaldi, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103114 · Cited by 33
Unsupervised Defect Segmentation of Magnetic Tile Based on Attention Enhanced Flexible U-Net
Cao, X., Chen, B., & He, W. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3170989 · Cited by 33
Sun, Q., Xu, K., Liu, H., & Wang, J. (2023) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2023.107674 · Cited by 31
Self-citationA Novel ST-YOLO Network for Steel-Surface-Defect Detection
Ma, H., Zhang, Z., & Zhao, J. (2023) · Sensors · doi:10.3390/s23229152 · Cited by 23
YOLO-DD: Improved YOLOv5 for Defect Detection
Wang, J., Wang, W., Zhang, Z., Lin, X., Zhao, J., Chen, M., & Luo, L. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.041600 · Cited by 19
Chaudhry, N. J., Khan, M. B., Iqbal, M. J., & Yasir, S. M. (2022) · Journal on artificial intelligence · doi:10.32604/jai.2022.038875 · Cited by 16
Surface Defect Recognition in Steel Plates Based on Impoved Faster R-CNN
Xie, Q., Zhou, W., Tan, H., & Wang, X. (2022) · Chinese Control Conference (CCC) · doi:10.23919/ccc55666.2022.9901872 · Cited by 10
A pixel-wise framework based on convolutional neural network for surface defect detection
Dong, G. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022408 · Cited by 9
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Sun, Q., Xu, K., Zhao, D., Li, H., Jin, L., Liu, C., & Xu, P. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128711 · Cited by 8
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Gong, K., Ding, X., Guo, F., Wang, Z., Zhang, F., & He, C. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3598383 · Cited by 8
Su, J., Zhang, H., Przystupa, К., & Кочан, О. В. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.3.033019 · Cited by 7
Multi-defect detection for magnetic tile based on SE-U-Net
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Xin, M., Xu, C., Gao, J., Wang, Y., & Wang, B. (2024) · Processes · doi:10.3390/pr12122940 · Cited by 4
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Tang, J., Zhang, A., & Liu, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-12339-2 · Cited by 3
Mentouri, Z., Bouguettaya, A., Doghmane, H., Gherfi, K., & Bendjoudi, S. (2024) · International Conference on Embedded Systems and Artificial Intelligence (ESAI) · doi:10.1109/esai62891.2024.10913550 · Cited by 3
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Tang, J., Cao, S., Chen, J., Song, T., Xu, Z., Zhou, Q., & Jiang, Q. Y. (2022) · Applied Sciences · doi:10.3390/app12126049 · Cited by 2
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Sun, H., Qian, T., Zou, S., Zhong, F., & Han, B. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-97064-2_12
Metadata
- Authors
- Liu, X., Xu, K., Zhou, P., Zhou, D., & Zhou, Y.
- Affiliations
- University of Science and Technology Beijing (CN)
- Corresponding author
- Xu, Ke
- Venue
- Optics and Lasers in Engineering
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 127
- Pages
- 105986
- ISSN
- 0143-8166 (ISSN-L)
- OpenAlex
- W2995986020
- Cited by
- 24
- References
- 37
- Citations by year
- 2025: 4 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 2 · 2020: 3
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Optics and Lasers in Engineering (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- aluminum strip, non-subsampled shearlet transform, cold rolling, defect classification, wrinkles, Scratch, quality inspection
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