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Sixth IEEE Workshop on Applications of Computer Vision (WACV'02)
Robust and Efficient Detection of Non-Lint Material in Cotton Fiber Samples
Orlando, Florida
December 03-December 04
ISBN: 0-7695-1858-3
Yupeng Zhang, The University of Tennessee
Philip W. Smith, The University of Tennessee
This paper describes the design of an automated image segmentation system that provides high-resolution measurements of non-lint material, or trash, in cotton samples. Unlike previous trash analysis systems, this platform is able to accurately and precisely quantify the amount of foreign matter present in a sample in the presence of both illuminant degradation and fibercolor variations by employing a new Bayesian Weighted K-Means (BWKM) approach to image segmentation. The design of the BWKM algorithm is presented in detail and its performance is verified and compared with other clustering techniques using both synthetic and real imagery.
Citation:
Yupeng Zhang, Philip W. Smith, "Robust and Efficient Detection of Non-Lint Material in Cotton Fiber Samples," wacv, pp.51, Sixth IEEE Workshop on Applications of Computer Vision (WACV'02), 2002
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