  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.
