Fabric Defect Detection Using a Hybrid and Complementary Fractal Feature Vector and FCM-based Novelty Detector

• Autorzy: Zhou J. , Wang J. , Bu H.

Identyfikatory

DOI

10.5604/01.3001.0010.5370

Warianty tytułu

PL

Wykrywanie defektów tkanin za pomocą hybrydowego wektora funkcji fraktalnej i nowatorskiego detektora opartego na zbiorze rozmytym wartości średnich (FCM)

• Języki publikacji: EN

Abstrakty

EN

Automated detect detection in woven fabrics for quality control is still a challenging novelty detection problem. This work presents five novel fractal features based on the box-counting dimension to address the novelty detection of fabric defect. Making use of the formation of woven fabric, the fractal features are extracted in a one-dimension series obtained by projecting a fabric image along the warp and weft directions, where their complementarity in discriminating defects is taken into account. Furthermore a new novelty detector based on fuzzy c-means (FCM) is devised to deal with one-class classification of the features extracted. Finally, by jointly applying the features proposed and the FCM based novelty detector, we evaluate the method proposed for eight datasets with different defects and textures, where satisfying results are achieved with a low overall missing detection rate.

PL

Automatyczne wykrywanie defektów tkanin w celu kontroli ich jakości mimo wielu dotychczasowych badań nadal stanowi wyzwanie. Mając na celu opracowanie nowatorskiej metody wykrywaniem wad tkanin przedstawiono pięć cech fraktalnych. W celu klasyfikacji wyodrębnionych cech opracowano detektor wad tkanin oparty na zbiorze rozmytym wartości średnich (FCM). Poprzez wspólne zastosowanie proponowanych cech i opartego na FCM detektorze sprawdzono proponowaną metodę dla ośmiu zestawów danych z różnymi defektami i teksturami. Stwierdzono, że otrzymane wyniki są na satysfakcjonującym poziomie.

Słowa kluczowe

EN

defect detection; box-counting dimension; fuzzy c-means; novelty detection

PL

wykrywanie defektów; wektor hybrydowy; zbiór rozmyty wartości średnich

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2017
• Tom: Vol. 25, no. 6 (126)
• Strony: 46--52
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Zhou J.

• Ministry of Education, Key Laboratory of Textile Science & Technology Donghua University, Shanghai, 201620, China
• Ministry of Education, Jiangnan University, Key Laboratory of Eco-textiles, Wuxi, Jiangsu 214122, China

autor

Wang J.

• Ministry of Education, Key Laboratory of Textile Science & Technology Donghua University, Shanghai, 201620, China

autor

Bu H.

• North Valley Aircraft, 11647 33rd St SE, Valley City, ND, 58072, USA

Bibliografia

• 1. Ngan H Y T, Pang G K H, Yung N H C. Automated fabric defect detection a review. Image and Vision Computing 2011; 7: 442–458.
• 2. Cohen F, Fan Z, Attali S. Automated inspection of textile fabrics using textural models. Pattern Analysis and Machine Intelligence 1991; 8: 803–808.
• 3. Baykut A, Atalay A, Erçil A, Güler M. Real-time defect inspection of textured surfaces. Real-Time Imaging 2000; 1: 17–27.
• 4. Latif-Amet A, Ertüzün A, Erçil A. An efficient method for texture defect detection: sub- band domain co-occurrence matrices. Image and Vision Computing 2000; 6: 543–553.
• 5. Chan C H, Pang G K H. Fabric defect detection by Fourier analysis. IEEE Transactions on Industry Applications 1999; 5: 1743-1750.
• 6. Yang X Z, Pang G, Yung N. Discriminative training approaches to fabric defect classification based on wavelet transform. Pattern Recognition 2004; 5: 889-899.
• 7. Kim S C, Kang T J. Texture classification and segmentation using wavelet packet frame and Gaussian mixture model. Pattern Recognition 2007; 4: 1207-1221.
• 8. Hou Z, Parker J M. Texture defect detection using support vector machines with adaptive gabor wavelet features. In: 7th IEEE Workshop on Applications of Computer Vision, 2005; pp. 275-280, New Jersey: IEEE.
• 9. Pentland A P. Fractal-based description of natural scenes. Pattern Analysis and Machine Intelligence 1984; 6: 661-674.
• 10. Lundahl T, Ohley W J, Kay S M, Siffert R. Fractal Brownian motion: A maximum likelihood estimator and its application to image texture. IEEE Transactions on Medical Imaging 1986; 3: 152-161.
• 11. Chen C C, Daponee J S, Fox M D. Fractal feature analysis and classification in medical imaging. Transactions on Medical Imaging 1989; 2: 133-142.
• 12. Conci A, Proença C B. Fractal image analysis system for fabric inspection based on a box- counting method. Computer Networks and ISDN Systems 1998; 20: 1887-1895.
• 13. Wen C Y, Chou S, Liaw J J. Textural defect segmentation using a fourier-domain maximum likelihood estimation method. Textile Research Journal 2002; 3: 253-258.
• 14. Bu H, Huang X. A novel multiple fractal features extraction framework and its application to the detection of fabric defects. Journal of the Textile Institute 2013; 5: 489-497.
• 15. Parrinello T, Vaughan R A. Multifractal Analysis and feature extraction in satellite imagery. International Journal of remote sensing 2002; 9: 1799-1825.
• 16. Lassouaoui N, Belouchrani A, Hamami-Mitiche L. On the use of multifractal analysis and genetic algorithms for the segmentation of cervical cell images. International Journal of Pattern Recognition and Artificial Intelligence 2003; 7: 1227-1244.
• 17. Bezdek J. C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
• 18. Balafar M A, Ramli A R, Iqbal S M, Mahmud R, Mashohor S, Balafar H. MRI segmentation of Medical images using FCM with initialized class centers via genetic algorithm. International Symposium on Information Technology. Kuala Lumpur, Malaysia, 2008, pp. 1-4.
• 19. Lee D J, Lee J P, Ji P S, Park J W, Lim J Y. Fault Diagnosis of Power Transformer Using SVM and FCM. Conference Record of the 2008 IEEE International Symposium on Electrical Insulation, Vancouver, BC, 2008, pp. 112-115.
• 20. Pruess S A Fractals in the earth sciences. New York: Springer, 1994.
• 21. Gao X B. Fuzzy clustering analysis and application. Xian: Xi Dian Press, 2004.
• 22. Pal N R, Bezdek J. C. On Clustering for the fuzzy c-means model. IEEE Transactions on Fuzzy System 1995; 3: 370-379.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).