15 February 2018 Three-dimensional measurement of yarn hairiness via multiperspective images
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Yarn hairiness is one of the essential parameters for assessing yarn quality. Most of the currently used yarn measurement systems are based on two-dimensional (2-D) photoelectric measurements, which are likely to underestimate levels of yarn hairiness because hairy fibers on a yarn surface are often projected or occluded in these 2-D systems. A three-dimensional (3-D) test method for hairiness measurement using a multiperspective imaging system is presented. The system was developed to reconstruct a 3-D yarn model for tracing the actual length of hairy fibers on a yarn surface. Five views of a yarn from different perspectives were created by two angled mirrors and simultaneously captured in one panoramic picture by a camera. A 3-D model was built by extracting the yarn silhouettes in the five views and transferring the silhouettes into a common coordinate system. From the 3-D model, curved hair fibers were traced spatially so that projection and occlusion occurring in the current systems could be avoided. In the experiment, the proposed method was compared with two commercial instruments, i.e., the Uster Tester and Zweigle Tester. It is demonstrated that the length distribution of hairy fibers measured from the 3-D model showed an exponential growth when the fiber length is sorted from shortest to longest. The hairiness measurements, such as H-value, measured by the multiperspective method were highly consistent with those of Uster Tester (r=0.992) but had larger values than those obtained from Uster Tester and Zweigle Tester, proving that the proposed method corrected underestimated hairiness measurements in the commercial systems.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Lei Wang, Bugao Xu, Weidong Gao, "Three-dimensional measurement of yarn hairiness via multiperspective images," Optical Engineering 57(2), 025103 (15 February 2018). https://doi.org/10.1117/1.OE.57.2.025103 . Submission: Received: 27 October 2017; Accepted: 19 January 2018
Received: 27 October 2017; Accepted: 19 January 2018; Published: 15 February 2018

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