Underwater Mussel Segmentation Using Smoothed Shape Descriptors with Random Forest

Author:

Valdez David Arturo Soriano,Azhar Mihailo,Strozzi Alfonso Gastelum,Hillman Jen,Thrush Simon,Delmas Patrice

Publisher

Springer Nature Switzerland

Reference31 articles.

1. Akhtar, A., Gao, W., Li, L., Li, Z., Jia, W., Liu, S.: Video-based point cloud compression artifact removal. IEEE Trans. Multimed. 24, 2866–2876 (2021)

2. Ali, J., Khan, R., Ahmad, N., Maqsood, I.: Random forests and decision trees. Int. J. Comput. Sci. Issues (IJCSI) 9(5), 272 (2012)

3. Azhar, M., Hillman, J.R., Gee, T., Thrush, S., Delmas, P.: A low-cost stereo pipeline for semi-automated spatial mapping of mussel structures within mussel beds. Remote Sens. Environ. (Manuscript in review) (2023)

4. Behley, J., et al.: Semantickitti: a dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9297–9307 (2019)

5. Chang, A.X., et al.: Shapenet: an information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015)

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