Large-Scale Fiber Tracking Through Sparsely Sampled Image Sequences of Composite Materials
Youjie Zhou, Hongkai Yu, Jeff P. Simmons, Craig Przybyla, Song Wang · IEEE Transactions on Image Processing · 2016
Fast and accurate characterization of fiber micro-structures plays a central role for material scientists to analyze physical properties of continuous fiber reinforced composite materials. In materials science, this is usually achieved by continuously cross-sectioning a 3D material sample for a sequence of 2D microscopic images, followed by a fiber detection/tracking algorithm through the obtained image sequence. To speed up this process and be able to handle larger size material samples, this paper proposes sparse sampling with larger inter-slice distance in cross sectioning and develops a new algorithm that can robustly track large-scale fibers from such a sparsely sampled image sequence. In particular, the problem is formulated as multi-target tracking, and the Kalman filters are applied to track each fiber along the image sequence. One main challenge in this tracking process is to correctly associate each fiber to its observation given that: fiber observations are of large scale, crowded, and show very similar appearances in a 2D slice and there may be a large gap between the predicted location of a fiber and its observation in the sparse sampling. To address this challenge, a novel group-wise association algorithm is developed by leveraging the fact that fibers are implanted in bundles and the fibers in the same bundle are highly correlated through the image sequence. In experiments, the proposed algorithm is tested on three tiles of 100-slice S200 material samples and the tracking performance is evaluated using 1136 human annotated ground-truth fiber tracks. Both quantitative and qualitative results show that the proposed algorithm clearly outperforms the state-of-the-art multiple-target tracking algorithms on sparsely sampled image sequences.