Fully residual Unet-based semantic segmentation of automotive fisheye images: a comparison of rectangular and deformable convolutions
Author:
Funder
ANR
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16627-9.pdf
Reference36 articles.
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2. Blott G, Takami M, Heipke C (2019) Semantic segmentation of fisheye images. In: Leal-Taix? L, Roth S (Eds.) Computer vision? ECCV 2018 workshops. Springer International Publishing, Cham. vol 11129, pp 181–196. https://doi.org/10.1007/978-3-030-11009-3_10. Series title: lecture notes in computer science
3. Brostow GJ, Fauqueur J, Cipolla R (2009) Semantic object classes in video: a high-definition ground truth database. Pattern Recognition Letters 30:88–97. https://doi.org/10.1016/j.patrec.2008.04.005. https://www.sciencedirect.com/science/article/pii/S0167865508001220
4. Brostow, GJ, Shotton J, Fauqueur J, Cipolla R (2008) Segmentation and recognition using structure from motion point clouds. In: ECCV (1), pp 44–57
5. Cheng Z, Liang J, Choi H, Tao G, Cao Z, Liu D, Zhang X (2022) Physical attack on monocular depth estimation with optimal adversarial patches. In: ECCV (38), pp 514–532. https://doi.org/10.1007/978-3-031-19839-7_30
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