Automatic Measurement and Recognition of Yarn Snarls by Digital Image and Signal Processing Methods

Bingang Xu, Charlotte Marion Murrells, Xiaoming Tao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)


In this paper, a computerized method has been proposed for automatic measurement and recognition of yarn wet snarls from an image of snarled yarn samples captured in a water bath. After image acquisition, image conversion and individual snarled sample extraction, the yarn profile function was extracted from the separated binary image. Fast Fourier Transform and Adaptive Orientated Orthogonal Projective Decomposition were then incorporated into a pattern recognition algorithm of yarn snarl features by treating the yarn profile function as a one-dimensional signal. In addition to the number of yarn snarl turns, the method was also accurate and efficient for the detection of yarn snarl height and width, which are unobtainable by the untwisting method. The effects of various factors on the yarn profile function were numerically examined, including distributions of yarn diameter and snarl, and the level of random noise.
Original languageEnglish
Pages (from-to)439-456
Number of pages18
JournalTextile Research Journal
Issue number5
Publication statusPublished - 1 Jan 2008


  • image processing
  • signal processing
  • twist liveliness
  • yarn snarling

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Polymers and Plastics

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