| Autor: |
Fu, Lan, Liu, Zhiyuan, Li, Jinlong, Simmons, Jeff, Yu, Hongkai, Wang, Song |
| Rok vydání: |
2023 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
Accurate detection of large-scale, elliptical-shape fibers, including their parameters of center, orientation and major/minor axes, on the 2D cross-sectioned image slices is very important for characterizing the underlying cylinder 3D structures in microscopic material images. Detecting fibers in a degraded image poses a challenge to both current fiber detection and ellipse detection methods. This paper proposes a new semi-supervised deep learning method for large-scale elliptical fiber detection with synthetic data, which frees people from heavy data annotations and is robust to various kinds of image degradations. A domain adaptation strategy is utilized to reduce the domain distribution discrepancy between the synthetic data and the real data, and a new Region of Interest (RoI)-ellipse learning and a novel RoI ranking with the symmetry constraint are embedded in the proposed method. Experiments on real microscopic material images demonstrate the effectiveness of the proposed approach in large-scale fiber detection. |
| Databáze: |
arXiv |
| Externí odkaz: |
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