Automated vision system for recognising lycra spandex defects

Fibres and Textiles in Eastern Europe

Volumn 84, Issue 1, 2011, Pages 43-46

  Su, Te Li ^a   Lu, Chieh Fu ^b  

^a NURSING AND MANAGEMENT COLLEGE   (Taiwan)

^b INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE   (Taiwan)

Author keywords

Automated vision recognition; Lycra spandex defects; Neural network; Taguchi method

Indexed keywords

BACKPROPAGATION; DEFECTS; ERRORS; IMAGE TEXTURE; NEURAL NETWORKS; POLYURETHANES; TAGUCHI METHODS;

AUTOMATED VISION SYSTEMS; BACK PROPAGATION NEURAL NETWORKS; FABRIC DEFECT DETECTION; FLAW CLASSIFICATION; GRAY LEVEL CO-OCCURRENCE MATRIX; LEARNING PARAMETERS; ROOT MEAN SQUARE ERRORS; VISION RECOGNITION;

MEAN SQUARE ERROR;

EID: 79551506173     PISSN: 12303666     EISSN: 23007354     Source Type: Journal    
DOI: None     Document Type: Article

References (7)

1. Srinivasan K., Dastoor P. H., Radhakrishnaiah, P., Jayaraman, S.; Journal of Textile Institute, Vol. 83(3), (1992) pp. 1431-1448.
2. Hu M.C., Tsai I.S.; Textile Research Journal, Vol. 70(8), (2000) pp. 662-670.
3. Tilocca A., Borzone P., Carosio S.; Textile Research Journal, Vol. 72(6), (2002) pp. 545-550.
4. Gadelmawla E. S.; NDT & E International, Vol. 37, (2004) pp. 577-588.
5. Taguchi G., Elsayed E. A., Hsiang T.; Quality engineering in production systems, McGraw-Hill, New York, 1989, pp. 1-9.
6. Taguchi G., Konishi S., Orthogonal arrays and linear graphs: tools for quality engineering, American Supplier Institute Inc., Michigan, 1987, p. 36.
7. Fowlkes W. Y., Creveling C. M.; Engineering methods for robust product design: using Taguchi methods in technology and product development, Addison-Wesley, Massachusetts, 1995, pp. 121-123.