Shaped Fiber Identification Using a Distance-Based Skeletonization Algorithm

Abstract

Fiber cross-sectional shapes can influence many physical properties of fibers. Automated identification of shaped fibers is critically important for fiber quality inspection. This paper presents a distance-based skeletonization algorithm used for reliable identification of shaped fibers. The skeleton of a fiber cross section, which is generated from fiber distance maps and maximal disks, is ensured to be continuous and insensitive to edge noise, and therefore can be used as abstract representations of fiber topology for shape analysis. A set of shape descriptors are defined from fiber skeletons and a support vector machine method is used to classify fibers based on the shape measurements. The experimental results show that the presented approach can be used to recognize shaped fibers based on the analysis of skeleton structures.