Image processing algorithm for automatic assessment of fabric shrinkage

Hamed Sari-Sarraf, Eric F. Hequet, Noureddine Abidi, Yongmei Dai, Hung Y. Chan, Michael R. Jasso, Ben Morris

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


A vision system for the automatic quantification of fabric geometric distortion has been implemented and tested. The intended utility of this system is to replace the manual measurement of fabric shrinkage or growth as governed by the AATCC (American Association of Textile Chemists and Colorists) Test Method 135. In the near future, other capabilities, such as automatic quantification of fabric smoothness, will also be incorporated. The system uses commercial, off-the-shelf hardware components, together with a customized image processing algorithm to capture digital images of pre-marked fabric swatches and to accurately measure the distance between the benchmarks before and after laundering. The primary focus of this paper is a description of the algorithm that detects these benchmarks. This robust algorithm detects the marks without regard to: (1) changes in the texture or the color of the swatches. (2) inter-fabric changes in the benchmark colors, (3) changes in the fabric contrast due to scanning or laundering. (4) presence of noise, or (5) slight rotations of the swatches during scanning. The presented system has been under routine testing at the International Textile Center of Texas Tech University, as well as the laboratories of Cotton Inc., with the computed dimensional changes and the manual measurements possessing a nearly perfect linear correlation.

Original languageEnglish
Pages (from-to)89-96
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2002


  • Adaptive thresholding
  • Color image processing
  • Edge-preserving smoothing
  • Fabric shrinkage assessment
  • Feature classification
  • Feature extraction


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