Feature pyramid with attention fusion for edge discontinuity classification-Reference-Cited by-同舟云学术

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Feature pyramid with attention fusion for edge discontinuity classification

Published:2023-03 Issue:2 Volume:34 Page:
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Sun Mingsi,Zhao Hongwei,Liu Pingping,Zhou Jianhang

Funder

Nature Science Foundation of China

Provincial Science and Technology Innovation Special Fund Project of Jilin Province

Jilin Province Development and Reform Commission Industrial Technology Research and Development Project

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s00138-023-01385-3.pdf

Reference40 articles.

1. Marr, D.: Vision: a computational investigation into the human representation and processing of visual information. Q. Rev. Biol. (1983). https://doi.org/10.1007/s10311-007-0116-z

2. Pu, M., Huang, Y., Guan, Q., Ling, H.: RINDNet: edge detection for discontinuity in reflectance, illumination, normal and depth. In: ICCV, pp. 6859–6868 (2021). https://doi.org/10.1109/ICCV48922.2021.00680

3. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 936–944 (2017). https://doi.org/10.1109/CVPR.2017.106

4. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018). https://doi.org/10.1109/TPAMI.2017.2699184

5. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(6), 679–698 (1986). https://doi.org/10.1109/TPAMI.1986.4767851

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