Invariant Feature Extraction Method Based on Smoothed Local Binary Pattern for Strip Steel Surface Defect
Open accessAbstract
An invariant feature extraction method based on smoothed local binary pattern (SLBP) is proposed for strip steel surface defect images. SLBP proposed in this paper is a developed version of local binary pattern (LBP). It is determined by the sign of the difference between weighted grays in local neighborhood. SLBP has the ability of noise smoothing. In this paper, invariant features are obtained by concentric discrete square sampling template (CDSST). Firstly, defect images are resampled on CDSST by the way of coordinate mapping. Then, invariant features in scale, rotation, illumination and translation are extracted by combining two types of SLBP images and gray-level co-occurrence matrix. Experimental results show that this novel feature extraction method not only can extract features with scale, rotation, illumination and translation invariance, but also can effectively suppress noise and maintain high classification accuracy.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398 · see citing works
Visual pattern recognition by moment invariants
Hu, M.-K. (1962) · IRE Transactions on Information Theory · doi:10.1109/tit.1962.1057692 · Cited by 7,594 · see citing works
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882 · see citing works
Twin Support Vector Machines for Pattern Classification
Jayadeva, Khemchandani, R., & Chandra, S. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1068 · Cited by 1,804 · see citing works
Description of interest regions with local binary patterns
Heikkilä, M., Pietikäinen, M., & Schmid, C. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.08.014 · Cited by 1,213 · see citing works
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 · see citing works
Multiscale image segmentation by integrated edge and region detection
Tabb, M., & Ahuja, N. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.568922 · Cited by 211 · see citing works
Switching Bilateral Filter With a Texture/Noise Detector for Universal Noise Removal
Lin, C.-H., Tsai, J.-S., & Chiu, C. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2047906 · Cited by 156 · see citing works
Strip Steel Surface Defect Classification Method Based on Enhanced Twin Support Vector Machine
Chu, M., Gong, R., & Wang, A. (2014) · ISIJ International · doi:10.2355/isijinternational.54.119 · Cited by 28 · see citing works
Self-citationChu, M., Wang, A., Gong, R., & Sha, M. (2014) · ISIJ International · doi:10.2355/isijinternational.54.1638 · Cited by 26 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 18
- [1]1. G. F. Wu, K. Xu and C. L. Yang: On-line Monitoring Technology for Steel Plate Surface Quality, Science Press, Beijing, (2010), 136 (in Chinese).
- [2]2. R. M. Haralick, K. Shanmugam and I. Dinstein: IEEE Trans. Syst., Man Cybern., SMC-3 (1973), 610.
- [3]3. Y. Hu, C. X. Zhao and H. N. Wang: Proc. Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Vol. 2, IEEE, Piscataway, NJ, (2008), 277.
- [4]4. M. K. Hu: IRE Trans. Inform. Theory, 8 (1962), 179.
- [5]5. T. Ojala, M. PietikÄainen and D. Harwood: Pattern Recognit., 29 (1996), 51.
- [6]6. T. Ojala, M. PietikÄainen and T. MÄaenpÄaÄa: IEEE Trans. Pattern Anal., 24 (2002), 971.
- [7]7. A. Hafiane, G. Seetharaman and B. Zavidovique: Proc. Int. Conf. on Image Analysis and Recognition, Springer-Verlag, Berlin Heidelberg, (2007), 387.
- [8]8. S. Liao and A. C. S. Chung: Proc. 8th Asian Conf. on Computer Vision, Springer-Verlag, Berlin, Heidelberg, (2007), 672.
- [9]9. A. Petpon and S. Srisuk: Proc. 5th Int. Conf. on Image and Graphics, IEEE, Piscataway, NJ, (2010), 533.
- [10]10. M. HeikkilÄa, M. PietikÄainen and C. Schmid: Pattern Recognit., 42 (2009), 425.
- [11]11. M. Pietikainen, T. Ojala and Z. Xu: Pattern Recognit., 33 (2000), 43.
- [12]12. M. X. Chu, A. N. Wang, R. F. Gong and M. Sha: ISIJ Int., 54 (2014), 1638.
- [13]13. C. H. Lin, J. S. Tsai and C. T. Chiu: IEEE Trans. Image Process., 19 (2010), 2307.
- [14]14. S. Wang: Proc. 11th Int. Conf. on Computer and Information Tech- nology, IEEE, Piscataway, NJ, (2011), 277.
- [15]15. M. Tabb and N. Ahuja: IEEE Trans. Image Process., 6 (1997), 642.
- [16]16. Jayadeva, R. Khemchandani and S. Chandra: IEEE Trans. Pattern Anal., 29 (2007), 905.
- [17]17. M. X. Chu, R. F. Gong and A. N. Wang: ISIJ Int., 54 (2014), 119.
- [18]18. E. Y. Hu, H. Wang, J. H. Wang, S. Lu and L. Tian: Proc. Int. Conf. on Computer Science and Automation Engineering, IEEE, Piscataway, NJ, (2011), 388.
Cited by
Sorted by citations
Automated Visual Defect Detection for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2963555 · Cited by 559 · see citing works
Review of surface defect detection of steel products based on machine vision
Tang, B., Chen, L., Sun, W. M., & Lin, Z. (2022) · IET Image Processing · doi:10.1049/ipr2.12647 · Cited by 298 · see citing works
Steel Surface Defect Recognition: A Survey
Wen, X., Shan, J., He, Y., & Song, K. (2022) · Coatings · doi:10.3390/coatings13010017 · Cited by 191 · see citing works
Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials
Fang, X., Luo, Q., Zhou, B., Li, C., & Tian, L. (2020) · Sensors · doi:10.3390/s20185136 · Cited by 166 · see citing works
Automated Visual Defect Classification for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148 · see citing works
A novel deep convolutional neural network algorithm for surface defect detection
Zhang, D., Hao, X., Liang, L., Liu, W., & Qin, C. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac071 · Cited by 109 · see citing works
A Compact Convolutional Neural Network for Surface Defect Inspection
Huang, Y., Qiu, C., Wang, X., Wang, S., & Yuan, K. (2020) · Sensors · doi:10.3390/s20071974 · Cited by 85 · see citing works
A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products
Ibrahim, A. A. M. S., & Tapamo, J. (2024) · Informatics · doi:10.3390/informatics11020025 · Cited by 49 · see citing works
Chu, M., Zhao, J., Liu, X., & Gong, R. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.07.008 · Cited by 44 · see citing works
Self-citationZhao, H., Wan, F., Lei, G., Xiong, Y., Xu, L., Xu, C., & Wen, Z. (2023) · Sensors · doi:10.3390/s23146558 · Cited by 33 · see citing works
Steel Strip Surface Defect Identification using Multiresolution Binarized Image Features
Mentouri, Z., Moussaoui, A., Boudjehem, D., & Doghmane, H. (2020) · Journal of Failure Analysis and Prevention · doi:10.1007/s11668-020-01012-7 · Cited by 28 · see citing works
Qiu, Y., Tang, L., Li, B., Niu, S., & Niu, T. (2020) · IEEE Access · doi:10.1109/access.2020.3032108 · Cited by 22 · see citing works
Liu, H., Hu, R., Dong, H., & Liu, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3548193 · Cited by 21 · see citing works
AFFNet: An Attention-Based Feature-Fused Network for Surface Defect Segmentation
Chen, X., Fu, C., Tie, M., Sham, C., & Ma, H. (2023) · Applied Sciences · doi:10.3390/app13116428 · Cited by 17 · see citing works
Recognition of Slab Identification Numbers using a Fully Convolutional Network
Lee, S. J., Kwon, W., Koo, G., Choi, H., & Kim, S. W. (2018) · ISIJ International · doi:10.2355/isijinternational.isijint-2017-695 · Cited by 16 · see citing works
Wen, X., Song, K., Niu, M., Dong, Z., & Yan, Y. (2016) · Optik · doi:10.1016/j.ijleo.2016.11.050 · Cited by 15 · see citing works
AEDN-YOLO: an efficient one-stage detection network for strip steel surface defects
Wei, M., Chen, B., Liu, J., Yuan, N., Liu, J., & Ji, Z. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad681d · Cited by 15 · see citing works
Lin, L., Wang, Y., Zhao, S., Liu, J., Zhang, S., & Zhang, G. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.6.063056 · Cited by 14 · see citing works
Improved cross pattern approach for steel surface defect recognition
Mentouri, Z., Doghmane, H., Moussaoui, A., & Bourouba, H. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-06050-x · Cited by 13 · see citing works
Surface Flaw Classification Based on Dual Cross Pattern
Mentouri, Z., Doghmane, H., Moussaoui, A., & Boudjehem, D. (2020) · International Conference on Communications, Control Systems and Signal Processing (CCSSP) · doi:10.1109/ccssp49278.2020.9151504 · Cited by 12 · see citing works
Support vector machine with quantile hyper-spheres for pattern classification
Chu, M., Liu, X., Gong, R., & Zhao, J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212361 · Cited by 12 · see citing works
Self-citationLearning a Class-specific and Shared Dictionary for Classifying Surface Defects of Steel Sheet
Zhou, S., Chen, Y., Zhang, D., Xie, J., & Zhou, Y. (2017) · ISIJ International · doi:10.2355/isijinternational.isijint-2016-478 · Cited by 9 · see citing works
Hong, D., Han, W.-H., & Yim, C. (2022) · Electronic Research Archive · doi:10.3934/era.2022204 · Cited by 7 · see citing works
Complex surface ROI detection for steel plate fusing the gray image and 3D depth information
Wen, X., Song, K., Huang, L., Niu, M., & Yan, Y. (2019) · Optik · doi:10.1016/j.ijleo.2019.163313 · Cited by 7 · see citing works
Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees
Shen, C., & Liu, Y. (2025) · Mathematics · doi:10.3390/math13152430 · Cited by 5 · see citing works
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 · see citing works
Shang, X., Song, W., Chen, Z., & Zhang, C. (2022) · IET Image Processing · doi:10.1049/ipr2.12608 · Cited by 2 · see citing works
Rajaram, V., Ingle, Y., Rajayogeshwar, G., Adishesha, P., & Lawrence, K. D. (2025) · Lecture notes in mechanical engineering · doi:10.1007/978-981-97-6176-0_7 · Cited by 2 · see citing works
Novel techniques for sparse representation problems
Chai, W. H. (2021) · doi:10.32657/10356/146392
Ono, H., Tate, M., Oshige, T., & Iizuka, Y. (2024) · 鐵と鋼 : 日本鐡鋼協會々誌 · doi:10.2355/tetsutohagane.tetsu-2024-009
EANet: Enhanced Attention-based Model for Surface Defect Detection
Liu, G. (2024) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac59436.2024.10503680
An efficient and lightweight image detection model based on YOLOv8 and its application
Chen, J., Zhao, J., & Sun, Y. (2025) · Workshop on Electronics Communication Engineering (WECE) · doi:10.1117/12.3061987
Ono, H., Tate, M., Oshige, T., & Iizuka, Y. (2025) · ISIJ International · doi:10.2355/isijinternational.isijint-2025-136
Seeing the unseen: unsupervised steel surface defect detection via image inpainting autoencoders
Bekiryazıcı, T., Aydemır, G., Avcı, A., Gürkan, H., Hanilçi, C., Akbaş, Y., & Öper, M. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05807-3
- Authors
- Chu, M., & Gong, R.
- Affiliations
- University of Science and Technology Liaoning (CN)
- Venue
- ISIJ International
- Publisher
- Iron and Steel Institute of Japan
- Published
- 2015
- Volume
- 55
- Issue
- 9
- Pages
- 1956-1962
- ISSN
- 0915-1559 (ISSN-L)
- 1347-5460
- OpenAlex
- W2181150946
- Semantic Scholar
- 7908f7997a5e5bf0c05a800639be5ac13ccfa707
- Cited by
- 34
- References
- 11
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 5 · 2023: 2 · 2022: 6 · 2021: 1 · 2020: 8 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 2
- FWCI
- 2.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- ISIJ International (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- University of Science and Technology Liaoning
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- local binary pattern, invariant feature extraction, gray level co-occurrence matrix, scale invariance, rotation invariance, illumination invariance, translation invariance, noise reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
