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All issues Volume 260 (2021) E3S Web Conf., 260 (2021) 03012 References
Open Access
Issue
E3S Web Conf.
Volume 260, 2021
2021 International Conference on Advanced Energy, Power and Electrical Engineering (AEPEE2021)
Article Number 03012
Number of page(s) 8
Section Electrical Engineering and Automation
DOI https://doi.org/10.1051/e3sconf/202126003012
Published online 19 May 2021
  1. R. Adams, and L. Bischof, “Seeded region growing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 6 (1994). [Google Scholar]
  2. L. Vincent, and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 6 (1991) [Google Scholar]
  3. F. F. Kong, and B. B. Song, “Improved YOLOv3 panoramic traffic monitoring target detection”. Computer Engineering and Applications, 56, 8 (2020) [Google Scholar]
  4. Z. J. Sun, L. Xue, Y. M. Xu, and Z. Wang, “Overview of deep learning,” Application Research of Computers, 29, 8 (2012) [Google Scholar]
  5. T.Y. Lin, Dollar, Piotr, G. Ross, K. M. He, H. Bharath, and B. Serge, “Feature Pyramid Networks for Object Detection,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, Hawaii, 2017) [Google Scholar]
  6. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2015). [Google Scholar]
  7. V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A deep convolutional encoder-decoder architecture for image segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 12 (2017) [Google Scholar]
  8. A. Paszke, A. Chaurasia, S. Kim and E. Culurciello, “ENet: A deep neural network architecture for real-time semantic segmentation,” arXiv preprint arXiv:1606.02147, 2016. [Google Scholar]
  9. R. Olaf, F. Philipp, and B. Thomas “U-Net: Convolutional Networks for Biomedical Image Segmentation” in the Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 234–241. 2015. [Google Scholar]
  10. S. Ghosh, N. C. Das, I. Das, and U. Maulik, “Understanding Deep Learning Techniques for Image Segmentation,” ACM Computing Surveys(CSUR), 2019. [Google Scholar]
  11. L. C. Chen, P. George, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs,” in the International Conference on Learning Presentations (2015) [Google Scholar]
  12. L. C. Chen, P. George, S. Florian, and A. Hartwig, “Rethinking Atrous Convolution for Semantic Image Segmentation.” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, Hawaii, 2017). [Google Scholar]
  13. L. C. Chen, Y. Zhu, P. George, S. Florian, A. Hartwig, “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” In the Proceedings of European Conference of Computer Vision, pp. 801–818 (Munich, Germany, 2018) [Google Scholar]
  14. Y. Fisher, K. Vladlen “Multi-Scale Context Aggregation by Dilated Convolutions,” in the International Conference on Learning Presentations (2015) [Google Scholar]
  15. H. S. Zhao, J. P. Shi, X. J. Qi, X. G. Wang, and J. Y. Jia. “Pyramid Scene Parsing Network,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, Hawaii, 2017). [Google Scholar]
  16. H. Y. Hou, T. Gao, and T. Li, “Overview of Image Segmentation Methods,” Computer Knowledge and Technology, 15, 5 (2019) [Google Scholar]
  17. H. J. Jun, B. S. Ko, Y. J. Kim, I. Kim, and J. Kim, “Combination of Multiple Global Descriptors for Image Retrieval,” arXiv:1903.10663 (2019) [Google Scholar]
  18. F. Z. Liu, Y. D. Xia, D. Yang, A. Yuille, and D. G. Xu, “An Alarm System for Segmentation Algorithm Based on Shape Model,” arXiv:1903.10645 (2019) [Google Scholar]
  19. J. M. Prewitt, “Object Enhancement and Extraction,” Picture Processing and Psychopictoric Press, 75–149 (1970) [Google Scholar]
  20. J. Canny “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 6 (1986) [Google Scholar]
  21. Y. Liu, M. M. Cheng, X. W. Hu, J. W. Bian, L. Zhang, X. Bai, and J.H. Tang, “Richer Convolutional Features for Edge Detection,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, Hawaii, 2017) [Google Scholar]
  22. D. Marmanis, K. Schindler, J. D. Wegner, S. Galliani, M. Dtcu, and U. Stiila, “Classification with an edge: improving semantic image segmentation with boundary detection,” ISPRS Journal of Photogrammetry and Remote Sensing, 135 158–172 (2018) [Google Scholar]
  23. Q. Huang, C. Y. Xia, W. C. Zheng, Y. H. Song, H. Xu, and K. C. C. Jay, “Object boundary guided semantic segmentation,” in Proceedings of the 13thAsian Conference on Computer Vision, (Springer, Berlin, 2016) [Google Scholar]
  24. X. N. Song, T. Rui, and X. Q. Wang, “Semantic segmentation method of road environment combined semantic boundary information,” Journal of Computer Applications, 39, 9 (2019) [Google Scholar]
  25. R. Sebastian “An Overview of multi-Task Learning in Deep Neural Networks,” arXiv: 1706.05098v1 (2017) [Google Scholar]
  26. K. Simonyan, and A. Zisserman, “Very deep convolutional networks for large-scale image recognition” in the International Conference on Learning Presentations (2015) [Google Scholar]
  27. Y. T. Lin, P. Goyal, R. Girshick, K. M. He, and P. Dollar, “Focal Loss for Dense Object Detection.” in 2019 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2019) [Google Scholar]
  28. M.A. Kramer, “Autoassociative neural networks,” Computers and Chemical Engineering, 16, 4 (1992) [Google Scholar]
  29. M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The Cityscapes Dataset for Semantic Urban Scene Understanding”. arXiv:1604.01695v2 (2016) [Google Scholar]
  30. K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, “Delving Deep into rectifiers: Surpassing Human-Level Performance on ImageNet Classification,” Presented at International Conference of Computer Vision (2016) [Google Scholar]

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