[섬유] Separation of Clustered Fibers in Cross-sectional Images using Image Set Theory 출판일 : 2009.12.01 저자 : Yan Wan, Li Yao, Bugao Xu, and Xiongying Wu 서지사항 : Textile Research Journal, Volume 79, Issue 18, 1658페이지 등록일 : 2011.04.29 I 조회수 : 75 작성자 : admin |
Fiber cross-sectioning often creates fiber clusters in microscopic images, in which fibers
touch or overlap each other. Prior to any geometrical analysis, it is critical to separate
touching/ overlapping fibers so that the features of individual fibers, not fiber clusters, can
be identified. Automatic separation of irregular, complex fiber cross-sections remains
challenging in image analysis for fiber characterization and measurements. This paper
introduces an algorithm based on the image set theory to separate clustered fibers in cross-
section images. An image is partitioned into three subsets, fiber edges, fiber interiors, and
background. The Euclidean distances between edge pixels and interior pixels are used to
assign the edge pixels to specific interiors. The assignment leads to the divisions among
the merged edge pixels. The experimental results demonstrated that the new algorithm can
optimally separate clustered fibers of various cross-sectional shapes, including W-shaped
and cross-shaped fibers.
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