Grey relational analysis of an automatic identifying system for clothing texture

• Autorzy: Su T. L. , Kuo Y.-L. , Chen H.-W. , Kung F.-C.

Warianty tytułu

PL

Analiza zbiorów rozmytych automatycznego systemu identyfikacji splotów tkackich

• Języki publikacji: EN

Abstrakty

EN

Fabric quality inspection is important to the textile industry because the price of second-quality fabric is merely 45% to 65% of that of first-quality fabric. Using the wavelet transform, this paper intends to analyse fabric images and establish the different features of fabric texture, and then through grey relational analysis of grey theory, we will attempt to distinguish and classify the texture of fabrics, mainly cotton, polyester, silk, rayon, knitting and linen. The grey relational analysis approach is applied to analyse the correlation in the random factor sequence of feature indexes after some data processing and determine the texture type of the designated fabric on the basis of the highest correlative degree. Experiment findings show that the automatic distinguishing system for the fabric types discussed in this paper is capable of distinguishing six different textile images.

PL

Kontrola jakości płaskich wyrobów włókienniczych jest bardzo ważna w przemyśle tekstylnym ze względu na różnice cenowe pomiędzy tekstyliami wyższej i niższej jakości. Wykorzystując odpowiednie przekształcenia matematyczne przeprowadzono analizę obrazów tekstyliów i na podstawie teorii zbiorów rozmytych zidentyfikowano struktury badanych próbek, w tym tekstyliów z bawełny, poliestru, jedwabiu, sztucznego jedwabiu i lnu. Ostateczną identyfikację badanej struktury przeprowadzono na podstawie współczynnika korelacji. Udowodniono, że automatyczny system rozróżniania rodzajów tekstyliów nadaje się do praktycznego zastosowania.

Słowa kluczowe

EN

fabric texture; fabric weave; automatic distinguishing system; wavelet transform; grey relational analysis

PL

tekstura tkanin; splot tkaniny; system identyfikacji; system automatyczny; transformacja falkowa; szara analiza relacyjna

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2010
• Tom: Vol. 18, no. 2 (79)
• Strony: 60--64
• Opis fizyczny: Bibliogr. 16 poz., fig.

Twórcy

autor

Su T. L.

• Department of Cosmetic Application and Management, St. Mary’s Medicine Nursing and Management College, Yilan County 266, Taiwan, Republic of China

autor

Kuo Y.-L.

• Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan, Republic of China

autor

Chen H.-W.

• Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan, Republic of China

autor

Kung F.-C.

• Center for General Education, Kainan University, Taoyuan County 338, Taiwan, Republic of China

Bibliografia

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• 2. Canoğlu Suat: Effect of First Heater Temperature Variations on the Polyester Yarn Properties of False -Twist Texturing Techniques, Fibres & Textiles in Eastern Europe, Vol. 17, No. 4, (2009), pp. 35-39.
• 3. Erol Rizvan, Sağbaş Aysun: Multiple Response Optimisation of the StapleYarn Production Process for Hairiness, Strength and Cost, Fibres & Textiles in Eastern Europe, Vol. 17, No. 5, (2009), pp. 40-42.
• 4. Brzeziński Stefan, Połowiński Stefan, Kowalczyk Dorota , Malinowska Grażyna: Effect of Corona Discharge Treatment on the Surface Strength and Performance Properties of Synthetic Fibre Textiles, Fibres & Textiles in Eastern Europe, Vol. 17, No. 5, (2009), pp. 62-68.
• 5. Struszczyk Marcin H., Rogaczewska Agnieszka, Dobrowolska Anna, Majcherek Zygmunt: Elaboration of the Optimal Structure of Flat Implants for Hernia Treatmentsn, Fibres & Textiles in Eastern Europe, Vol. 17, No. 1, (2009), pp. 103-108.
• 6. Wilson B., Bayoumi M.A.: Compresseddomain Classifi cation of Texture Images, Proceedings of the 2000 Fifth IEEE International Workshop on Computer Architectures for Machine Perception, Padova, Italy, (2000), pp. 347-355.
• 7. Tsai I.S., Hu M.C.: Automatic Inspection of Fabric Defects Using an Artificial Neural Network Technique, Textile Research Journal, Vol. 66, No. 7, (1996), pp. 474-482.
• 8. Shiau Y.R., Tsai I.S., Lin C.S.: Classifying Web Defects with a Back-Propagation Neural Network by Color Image Processing, Textile Research Journal, Vol. 70, No. 7, (2000), pp. 633-640.
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• 10. Iyengar S.S., Cho E.C., Phoha V.V.: Foundations of Wavelet Networks and Applications, Chapman & Hall/CRC, Florida, 2002.
• 11. Kuo C.F.J., Su T.L., Chang C.D., Lee C.H.: Intelligence Control of On-line Dynamic Gray Cloth Inspecting Machine System Module Design. II. Defects Inspecting Module Design, Fibers and Polymers, Vol. 9, No. 6, (2008), pp. 768-775.
• 12. Su T.L., Kung F.C., Kuo Y.L.: Application of Back-propagation Neural Network Fuzzy Clustering in Textile Texture Automatic Recognition System, Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 1, art. no. 4635748, pp. 46-49.
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• 14. Kuo C.F.J., Su T.L.: Gray Relational Analysis for Recognizing Fabric Defects, Textile Research Journal, Vol. 73, No. 5, (2003), pp. 461-465.
• 15. Kuo C.F.J., Su T.L., Tsai C.P.: Optimization of the Needle Punching Process for the Nonwoven Fabrics with Multiple Quality Characteristics by Grey-based Taguchi Method, Fibers and Polymers, Vol. 8, No. 6, (2007), pp. 654-664.
• 16. Kuo C.F.J., Su T.L.: Optimization of Multiple Quality Characteristics for Polyether Ether Ketone Injection Molding Process, Fibers and Polymers, Vol. 7, No. 4, (2006), pp. 404-413.