Image Semantic Segmentation Based on Joint Normalization
Author:
Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-6554-7_13
Reference13 articles.
1. Yan, J., Zhong, Y., Fang, Y., Wang, Z., Ma, K.: Exposing semantic segmentation failures via maximum discrepancy competition. Int. J. Comput. Vis. 129(5), 1768–1786 (2021). https://doi.org/10.1007/s11263-021-01450-2
2. Budak, U., Çıbuk, M., Cömert, Z., Şengür, A.: Efficient COVID-19 segmentation from CT slices exploiting semantic segmentation with integrated attention mechanism. J. Digit. Imaging 5, 1 (2021). https://doi.org/10.1007/s10278-021-00434-5
3. Zhang, J., Xiaoli, D., Xie, Y., Jianjia, D., Fuyong, H., Zeyu, Z.: A semantic segmentation method for buffer layer defect detection in high voltage cable. In: E3S Web of Conferences, vol. 233 (2021)
4. Jin, Z., Zhang, Z., Ott, J., Gu, G.X.: Precise localization and semantic segmentation detection of printing conditions in fused filament fabrication technologies using machine learning. Addit. Manufact. 37, 101696 (2021)
5. Kose, K., et al.: Segmentation of cellular patterns in confocal images of melanocytic lesions in vivo via a multiscale encoder-decoder network (MED-Net). Med. Image Anal. 67, 101841 (2021)
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