Preliminary validation of a fabric smoothness assessment system

A fabric's tendency to wrinkle is vitally important to the textile industry as it impacts the visual appeal of apparel. Current methods of grading this characteristic, called fabric smoothness, are very subjective and inadequate. As such, a quantitative method for assessing fabric smoothness is of the utmost importance to the textile community. To that end, we propose a laser-based surface-profiling system that utilizes a smart camera to sense the 3-D topography of fabric specimens. The system incorporates methods based on anisotropic diffusion and the facet model for characterizing edge information that ultimately relate to a specimen's degree of wrinkling. We detail the initial steps in a large-scale validation of this system. Using histograms of the extracted features, we compare the output of the system between two studies that total more than 200 fabric specimens. The results show that with the features used so far, this system is at least as good as the current American Association of Textile Chemists and Colorists (AATCC) smoothness grading system.

[1]  Hamed Sari-Sarraf,et al.  Preliminary validation of a fabric smoothness assessment system , 2003, International Conference on Quality Control by Artificial Vision.

[2]  Bugao Xu,et al.  Evaluating Fabric Smoothness Appearance with a Laser Profilometer , 1998 .

[3]  Philippe Saint-Marc,et al.  Adaptive Smoothing: A General Tool for Early Vision , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Tae Jin Kang,et al.  New Objective Evaluation of Fabric Smoothness Appearance , 2001 .

[5]  Tae Jin Kang,et al.  A New Objective Method of Measuring Fabric Wrinkles Using a 3-D Projecting Grid Technique , 1999 .

[6]  Youngjoo Na,et al.  Assessing Wrinkling Using Image Analysis and Replicate Standards , 1995 .

[7]  Bugao Xu,et al.  Fabric wrinkle evaluation using laser triangulation and neural network classifier , 1999 .

[8]  X. B. Huang,et al.  Evaluating Fabric Wrinkle Degree with a Photometric Stereo Method , 2003 .

[9]  C. Turner,et al.  Automatic assessment of fabric smoothness , 2002, The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002..

[10]  Mohan M. Trivedi,et al.  3-D Computer Vision Using Structured Light: Design, Calibration, and Implementation Issues , 1996, Adv. Comput..

[11]  Richard Kitney,et al.  An Edge Detection Technique Using the Facet Model and Parameterized Relaxation Labeling , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Z. Fazekas,et al.  Automatic visual assessment of fabric quality , 1999, ISIE '99. Proceedings of the IEEE International Symposium on Industrial Electronics (Cat. No.99TH8465).

[13]  Bugao Xu,et al.  Instrumental Evaluation of Fabric Wrinkle Recovery , 1995 .

[14]  Mitsuo Matsudaira,et al.  Objective Evaluation for Wrinkle Appearance of Fabrics by Image Processing and Slit Beam Projecting Technique , 2003 .

[15]  Robert M. Haralick,et al.  Integrated Directional Derivative Gradient Operator , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  Ching Y. Suen,et al.  A fast parallel algorithm for thinning digital patterns , 1984, CACM.

[17]  Toshio Mori,et al.  Evaluating Wrinkled Fabrics with Image Analysis and Neural Networks , 2002 .

[18]  D. M. Lewis,et al.  A new agent for cotton durable press finishing under alkaline conditions , 2002 .

[19]  R. Haralick.  Digital Step Edges from Zero Crossing of Second Directional Derivatives , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..