A Fine Segmentation Method for the Outer Boundary of Tire Image Steel Belt Based on Adaptive Thresholding Under Local Histogram Statistical Features
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0855-0_11
Reference22 articles.
1. Zhang, Y., Gu, N., Zhang, X., Lin, C.: Tire X-ray image defects detection based on adaptive thresholding method. In: Shen, H., Sang, Y. (eds.) Parallel Architectures, Algorithms and Programming. PAAP 2019. Communications in Computer and Information Science, vol. 1163, pp. 118–129. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-2767-8_11
2. Zhang, Y., Cui, X., Liu, Y., et al.: Tire defects classification using convolution architecture for fast feature embedding. Int. J. Comput. Intell. Syst. 11(1), 1056–1066 (2018)
3. Wang, Y., et al.: Unsupervised learning with generative adversarial network for automatic tire defect detection from x-ray images. Sensors 21(20), 6773(2021)
4. Zheng, Z., Zhang, S., Yu, B., Li, Q., et al.: Defect inspection in tire radiographic image using concise semantic segmentation. IEEE Access 8, 112674–112687 (2020)
5. Yu, X., Ding, J., Li, J.: Detection of overlapping and cross-defects of tire ply lines. J. Univ. Jinan 31(6), 494–498 (2017)
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