TY - JOUR
T1 - Image processing algorithm for automatic assessment of fabric shrinkage
AU - Sari-Sarraf, Hamed
AU - Hequet, Eric F.
AU - Abidi, Noureddine
AU - Dai, Yongmei
AU - Chan, Hung Y.
AU - Jasso, Michael R.
AU - Morris, Ben
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
KW - Adaptive thresholding
KW - Color image processing
KW - Edge-preserving smoothing
KW - Fabric shrinkage assessment
KW - Feature classification
KW - Feature extraction
UR - http://www.scopus.com/inward/record.url?scp=0036407533&partnerID=8YFLogxK
U2 - 10.1117/12.460186
DO - 10.1117/12.460186
M3 - Article
AN - SCOPUS:0036407533
SN - 0277-786X
VL - 4664
SP - 89
EP - 96
JO - Proceedings of SPIE - The International Society for Optical Engineering
JF - Proceedings of SPIE - The International Society for Optical Engineering
ER -