A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 11
Issue 2
IEEE/CAA Journal of Automatica Sinica
| Citation: | L. Yan, Q. Li, and K. Li, “Object helps U-Net based change detectors,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 548–550, Feb. 2024. doi: 10.1109/JAS.2023.124032
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| [1] |
L. Yan, W. Zheng, and F.-Y. Wang, “Heterogeneous image knowledge driven visual perception,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 255–257, 2024. doi: 10.1109/JAS.2023.123435
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| [2] |
W. Zheng, L. Yan, C. Gou, and F.-Y. Wang, “Computational knowledge vision: Paradigmatic knowledge based prescriptive learning and reasoning for perception and vision,” Artificial Intelligence Review, vol. 55, no. 8, pp. 5917–5952, 2022.
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| [3] |
Z. Qin, X. Lu, X. Nie, D. Liu, Y. Yin, and W. Wang, “Coarse-to-fine video instance segmentation with factorized conditional appearance flows,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1192–1208, 2023. doi: 10.1109/JAS.2023.123456
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| [4] |
Y. Wang, P.-M. Jodoin, F. Porikli, J. Konrad, Y. Benezeth, and P. Ishwar, “CDnet 2014: An expanded change detection benchmark dataset,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, 2014, pp. 387–394.
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| [5] |
M. Duan, K. Li, X. Liao, and K. Li, “A parallel multiclassification algorithm for big data using an extreme learning machine,” IEEE Trans. Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2337–2351, 2018. doi: 10.1109/TNNLS.2017.2654357
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| [6] |
L. A. Lim and H. Y. Keles, “Learning multi-scale features for foreground segmentation,” Pattern Analysis and Applications, vol. 23, pp. 1369–1380, 2020. doi: 10.1007/s10044-019-00845-9
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| [7] |
W. Zheng, K. Wang, and F.-Y. Wang, “A novel background subtraction algorithm based on parallel vision and bayesian GANS,” Neurocomputing, vol. 394, pp. 178–200, 2020. doi: 10.1016/j.neucom.2019.04.088
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| [8] |
W. Zheng, K. Wang, and F. Wang, “Background subtraction algorithm based on bayesian generative adversarial networks,” ACTA Automatica Sinica, vol. 44, no. 5, pp. 878–890, 2018.
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| [9] |
G. Rahmon, F. Bunyak, G. Seetharaman, and K. Palaniappan, “Motion U-Net: Multi-cue encoder-decoder network for motion segmentation,” in Proc. Int. Conf. Pattern Recognition, 2021, pp. 8125–8132.
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| [10] |
O. Tezcan, P. Ishwar, and J. Konrad, “BSUV-Net: A fully-convolutional neural network for background subtraction of unseen videos,” in Proc. IEEE/CVF Winter Conf. Applications of Computer Vision, 2020, pp. 2774–2783.
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| [11] |
L. A. Lim and H. Y. Keles, “Foreground segmentation using convolutional neural networks for multiscale feature encoding,” Pattern Recognition Letters, vol. 112, pp. 256–262, 2018. doi: 10.1016/j.patrec.2018.08.002
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| [12] |
G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics YOLOv8,” 2023.[Online]. Available: https://github.com/ultralytics/ultralytics.
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| [13] |
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE TPAMI, vol. 39, no. 6, pp. 1137–1149, 2017. doi: 10.1109/TPAMI.2016.2577031
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