آشكارسازي عيوب بافتي پارچه با استفاده از شكل بهبوديافته الگوي باينري محلي
محورهای موضوعی : مهندسی برق و کامپیوتر
فرشاد تاجری‌پور
1
*
,
احسان‌اله کبیر
2
,
عباس شیخی
3
1 - دانشگاه تربیت مدرس
2 - دانشگاه تربیت مدرس
3 - دانشگاه شیراز
کلید واژه: آشكارسازيالگوي باينري محليبافتبينایي ماشينپارچهعيوب بافتي,
چکیده مقاله :
يكي از روش‌هايي كه در عين سادگي مي‌تواند ويژگي‌هاي مناسبي براي طبقه‌بندي بافت تصوير با دقت بالا توليد كند، الگوي باينري محلي است. در اين مقاله روشي براي آشكارسازي عيوب بافتي پارچه با استفاده از اين ويژگي‌ها ارائه شده است. ابتدا در مرحله آموزش، عملگر الگوي باينري محلي روي كل تصوير پارچه سالم پيكسل به پيكسل اعمال مي‌شود و بردار ويژگي‌هاي مبنا به دست مي‌آيد. سپس اين تصوير به پنجره‌هايي تقسيم شده و عملگر الگوي باينري محلي روي هر كدام از اين پنجره‌ها اعمال شده و بر اساس مقايسه با بردار ويژگي مبنا يك حد آستانه مناسب براي سالم‌بودن پنجره‌ها محاسبه مي‌شود. در هنگام آشكارسازي، تصوير مورد بررسي به پنجره‌هايي تقسيم شده و با استفاده از حد آستانه محاسبه‌شده، پنجره‌هايي كه به قسمت‌هاي معيوب تصوير تعلق دارند مشخص مي‌شود. روش ارائه‌شده نسبت به انتقال تصوير و تغيير شدت روشنايي نقاط تصوير حساس نيست و از آن مي‌توان براي آشكارسازي عيوب بافتي در پارچه‌هاي بدون طرح و پارچه‌هاي طرح‌دار استفاده كرد. با توجه به سادگي روش، پياده‌سازي آن به صورت برخط ميسر است. نتايج به دست آمده نشان مي‌دهد كه دسته وسيعي از عيوب بافتي پارچه با اين روش به صورت مطلوب قابل آشكارسازي هستند.
One of the methods which can produce powerful features for texture classification is Local Binary Patterns, LBP. In this paper we propose a method for defect detection in textile fabrics using these features. In the training stage, at first step LBP operator is applied to an image of defect free fabric, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied on each of these windows. Based on comparison to the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is gray scale and shift invariant and can be used for defect detection in patterned and plain fabrics. Due to its simplicity online implementation is possible.
[1] C. S. Cho, B. M. Chung, and M. J. Park, "Development of real-time vision-based fabric inspection system," IEEE Trans. Ind. Elec., vol. 52, no. 4, pp. 1073-1079, Aug. 2005.
[2] J. W. Roberts, S. D. Rose, G. A. Jullian, G. A. Nichols, L. Jenkins, P. Chamberlain, and S. G. Maroscher, "PC based real-time defect imaging system for high speed web inspection," in Proc. SPIE, vol. 1907, pp. 164-176, Feb. 1993.
[3] J. Laitinen, "Image quality in automated visual web inspection," in Proc. SPIE, vol. 3029, pp. 78-89, Apr. 1997.
[4] R. W. Conners, C. W. McMillin, K. Lin, and R. E. Vasquez-Espinosa, "Identifying and locating surface defects in wood: Part of an automated lumber processing system," IEEE Trans. Pattern Anal. Machine Intell., vol. 5, no. 6, pp. 573-583, Nov. 1983.
[5] E. Young, "Use of line scan cameras and a DSP processing system for high-speed wood inspection," in Proc. SPIE, vol. 2597, pp. 259-264, Oct. 1995.
[6] L. Siew, R. M. Hodgson, and E. J. Wood, "Texture measures for carpet wear assessment," IEEE Trans. Pattern Anal. Machine Intell., vol. 10. no. 1, pp. 92-105, Jan. 1988.
[7] S. H. Sheen, H. T. Chien, W. P. Lawrence, and A. C. Raptis, Ultrasonic Imaging System for In-Process Fabric Defect Detection, U. S. Patent 5665 907, Sep. 1997.
[8] L. Dorrity and G. Vachtsevanos, "In-process fabric defect detection and identification," presented at the Mechatronics’98, vol. 65, no. 3, pp. 123-130, Skovde, Sweden, Sep. 1998.
[9] I. S. Tsai and M. C. Hu, "Automated inspection of fabric defects using an artificial neural networks," Text. Res. J., vol. 66, no. 4, pp. 474-482, Jul. 1996.
[10] H. Sari-Sarraf and J. S. Goddard, "Vision systems for on-loom fabric inspection," IEEE Trans. Ind. Applicat., vol. 35, no. 6, pp. 1252-1259, Nov./Dec. 1999.
[11] A. Kumar and G. Pang, "Fabric defect segmentation using multi-channel blob detectors," Opt. Eng., vol. 39, no. 12, pp. 3176-3190, Dec. 2000.
[12] F. S. Cohen, Z. Fan, and S. Attali, "Automated inspection of textile fabrics using textural models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 8, pp. 803-808, Aug. 1991.
[13] A. Atalay, Automated Defect Inspection of Textile Fabrics Using Machine Vision Techniques, M.S. Thesis, Bogazici University, Istanbul, Turkey, 1995.
[14] A. Bodnarova, M. Bennamoun, and K. K. Kubik, "Defect detection in textile materials based on aspects of HVS," in Proc. IEEE SMC’98 Conf., vol. 5, pp. 4423-4428, San Diego, US, Oct. 1998.
[15] J. Chen and A. K. Jain, "A structural approach to identify defects in textural images," in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, vol. 1, pp. 29-32, Aug. 1988.
[16] M. Bennamoun and A. Bodnarova, "Automatic visual inspection and flaw detection in textile materials: Past, present and future," in Proc. IEEE Conf. on Systems, Man, and Cybernetics, vol. 5, pp. 4340-4343, Oct. 1998.
[17] A. Kumar and G. K. H. Pang, "Defect detection in textured materials using Gabor filters," IEEE Trans. Ind. Appl., vol. 38, no. 2, pp. 425-440, Mar./Apr. 2002.
[18] T. Randen and J. H. Husy, "Texture segmentation using filters with optimized energy separation," IEEE Trans. Image Processing, vol. 8, no. 4, pp. 571-582, Apr. 1999.
[19] C. H. Chan and G. Pang, "Fabric defect detection by Fourier analysis," IEEE Trans. Ind. Applicat., vol. 36, no. 5, pp. 1267-1276, Sep./Oct. 2000.
[20] H. Y. T. Ngan, G. K. H. Pang, S. P. Yung, and M. K. Ng, "Defect detection on patterned jacquard fabric," IEEE AIPR'03, vol. 4, no. 3, pp. 163-168, Oct. 2003.
[21] T. Ojala, M. Pietik¨ainen, and T. M¨aenp¨a¨a, "Multiresolution gray scale and rotation invariant texture analysis with local binary patterns,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, Jul. 2002.
[22] H. Y. T. Ngan, G. K. H. Pang, S. P. Yung, and M. K. Ng, "Wavelet based method on patterned fabric defect detection," Pattern Recognition, vol. 38, pp. 559-576, Apr. 2005.
[23] A. Bodnarova, M. Bennamoun, and K. K. Kubik, "Suitability analysis of techniques for flaw detection in textiles using texture analysis," Pattern Analysis & Applications, vol. 3, pp. 254-266, Sep. 2000.
[24] S. Arivazhagan, L. Ganesan, and S. Bama, "Fault segmentation in fabric images using Gabor wavelet transform," Machine Vision and Applications, vol. 16, no. 6, pp. 356-363, Feb. 2006
[25] Grantiteville Company, Manual of Standard Fabric Defects in the Textile Industry, Grantiteville Company, South Carolina, US, 1975.
[26] T. Randen and J. H. Husoy, "Filtering for texture classification: a comparative study," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291-310, Apr. 1999.
[27] A. Latif-Amet, A. Ertüzün, and A. Erçil, "An efficient method for texture defect detection: sub-band domain co-occurrence matrices," Image Vision Comput., vol. 18, no. 6, pp. 543-553, May 2000.