Öz
The foreign
substances, arising during the production and shaping of wool and cotton raw
materials that are used in textile and cotton gin factories or coming from the
outside, decrease considerably the quality of the obtained fabric or yarn.
Nowadays, a different methods are used to separate foreign substances in the
textile sector, most of these methods are not efficient in terms of speed and
quality. Computerized vision systems play a vital role in the field of textiles
as in other fields. In this study, Intuitionistic Fuzzy Algorithm is used to
define the foreign substances in the images that obtained from a camera. CPU
(Central Processing Unit) based applications have speed problems due to the
structure of the algorithm. For this reason, GPU (Graphics Processing Unit)
technology was used to overcome the speed problem. The otsu algorithm generates
a dynamic threshold from the numerical values of the image obtained using the
Intuitionistic fuzzy algorithm. By this means, the threshold value of each
frame obtained from the camera was calculated on real time and implemented on
the image timely. These algorithms were accelerated maximum 262 times using
NVIDIA GTX 480 GPU supported display card.
Anahtar Kelimeler
GPU Programming Intuitionistic Fuzzy Otsu CUDA Foreign Fiber Detection
Destekleyen Kurum
Kahramanmaraş Sütçü İmam Üniversitesi
Proje Numarası
2011/3-31YLS
Teşekkür
This study was supported by the Scientific Research Project of Kahramanmaraş Sütçü Imam University with the code of 2011/3-31YLS
Kaynakça
- Alam, I. J. (2013). Detecting Edge in an Image with the Help of Fuzzy Parameters. 11th International Conference on Frontiers of Information Technology, (pp. 19-24). Islamabad, Pakistan.Atanossov, K. (1986). Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 20, 87-96.Bahri, H. S. (2017). Image feature extraction algorithm based on CUDA architecture: case study GFD and GCFD. IET Computers & Digital Techniques, 11(4), 125-132.Chaira, T. R. (2008). A new measure using intuitionistic fuzzy set theory and its application to edge detection. Applied Soft Computing, 8(2), 919–927.Chen, Z. X. (2010). A New High-Speed Foreign Fiber Detection System with Machine Vision. Mathematical Problems in Engineering, Article ID 398364, 15 pages.Fan, J. X. (1999). Distance measure and induced fuzzy entropy. Fuzzy Sets and Systems, 104, 305-314.Faujdar, N. G. (2017). A practical approach of GPU bubble sort with CUDA hardware. 7th International Conference on Cloud Computing (pp. 7-12). Noida: Data Science & Engineering.Gunes, M. B. (2016). Detecting Direction of Pepper Stem by Using CUDA-Based Accelerated Hybrid Intuitionistic Fuzzy Edge Detection and ANN. Journal of Sensors, 11 pages.Ji, R. L. (2010). Classification and Identification of Foreign Fibers in Cotton on the Basis of a Support Vector Machine. Mathematical and Computer Modelling, 51, 1433-1437.Kaushik, R. B. (2015). On Intuitionistic Fuzzy Divergence Measure with Application to Edge Detection. Procedia Computer Science, 70, 2-8.Liberman, M. A. (1998). Determining gravimetric bark content in cotton with machine vision. Textile Research Journal, 68(2), 94-104.Millman, M. P. (2001). Computer vision for textured yarn interlace (nip) measurements at high speeds. Mechatronics, 11(8), 1025-1038.NVIDIA, C. (2019). CUDA C Programming GUIDE. Retrieved from 9th Edition: https://docs.nvidia.com/cuda/cuda-c-programming-guide/Otsu, N. A. (1979). Threshold Selection Method from Gray-level Histograms. IEEE Transactions on Systems, Man and Cybernetic, 9(1), 62-66.Pal, N. R. (1992). Some Properties of the Exponential Entropy. Inform. Sci., 66, 119-137.Tastaswadi, P. V. (1999). Machine vision for automated visual inspection of cotton quality in textile industries using color isodiscrimination contour. Computer Industrial Engineering, 37(1-2), 347-350.Wang, X. Y. (2015). A fast image segmentation algorithm for detection of pseudo-foreign fibers in lint cotton. Comput. Electr. Eng., 46, 500-510.Xuecheng, L. (1992). Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets and Systems, 52, 305-318.Yang, W. Z. (2009). A new approach for image processing in foreign fiber detection. Computers and Electronics in Agriculture, 68(1), 68-77.Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.Zhang, H. L. (2014). Applications of computer vision techniques to cotton foreign matter inspection: A review. Computers and Electronics in Agriculture, 109, 59-70.Zhang, X. L. (2011). A fast Segmentation Method for High-Resulation Color Images of Foreign Fibers in Cotton. Computers and Electronics in Agriculture, 78, 71-79.
Pamuktaki Yabancı Elyafların GPU ile Hızlandırılmış Sezgisel Bulanık Mantık ve Otsu Algoritmaları ile Tesbiti
Öz
Tekstil,
pamuk ve çırçır fabrikalarında kullanılan veya dışarıdan gelen yün ve pamuk ham
maddelerinin üretimi ve şekillendirilmesinde ortaya çıkan yabancı maddeler,
elde edilen kumaş veya ipliğin kalitesini önemli ölçüde azaltır. Günümüzde
tekstil sektöründeki yabancı maddeleri ayırmak için farklı yöntemler
kullanılmaktadır, ancak bu yöntemlerin
çoğu hız ve kalite açısından verimli değildir. Bilgisayarlı görme sistemleri,
diğer alanlarda olduğu gibi tekstil alanında da hayati bir rol oynamaktadır. Bu
çalışmada, kameradan elde edilen görüntülerdeki yabancı maddeleri tanımlamak
için Sezgisel Bulanık Mantık kullanılmıştır. CPU tabanlı uygulamalar ilgili
algoritmanın yapısı gereği hız problemlerine yol açmaktadır. Bu hız problemini
gidermek için ise GPU teknolojisi kullanılmıştır. Otsu algoritması kullanarak
Sezgisel bulanık mantık algoritmasıyla elde edilen görüntüler için dinamik bir
eşik değeri hesaplanmıştır. Bu sayede, kameradan elde edilen her karenin eşik
değeri gerçek zamanlı olarak hesaplanmış ve görüntüye aynı anda uygulanmıştır.
Bu algoritmalar, NVIDIA GTX 480 GPU destekli ekran kartı kullanılarak maksimum
262 kez hızlandırılmıştır.
Anahtar Kelimeler
GPU Programlama Sezgisel Bulanık Mantık Otsu CUDA Yabancı Elyaf Tespit Etme
Proje Numarası
2011/3-31YLS
Kaynakça
- Alam, I. J. (2013). Detecting Edge in an Image with the Help of Fuzzy Parameters. 11th International Conference on Frontiers of Information Technology, (pp. 19-24). Islamabad, Pakistan.Atanossov, K. (1986). Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 20, 87-96.Bahri, H. S. (2017). Image feature extraction algorithm based on CUDA architecture: case study GFD and GCFD. IET Computers & Digital Techniques, 11(4), 125-132.Chaira, T. R. (2008). A new measure using intuitionistic fuzzy set theory and its application to edge detection. Applied Soft Computing, 8(2), 919–927.Chen, Z. X. (2010). A New High-Speed Foreign Fiber Detection System with Machine Vision. Mathematical Problems in Engineering, Article ID 398364, 15 pages.Fan, J. X. (1999). Distance measure and induced fuzzy entropy. Fuzzy Sets and Systems, 104, 305-314.Faujdar, N. G. (2017). A practical approach of GPU bubble sort with CUDA hardware. 7th International Conference on Cloud Computing (pp. 7-12). Noida: Data Science & Engineering.Gunes, M. B. (2016). Detecting Direction of Pepper Stem by Using CUDA-Based Accelerated Hybrid Intuitionistic Fuzzy Edge Detection and ANN. Journal of Sensors, 11 pages.Ji, R. L. (2010). Classification and Identification of Foreign Fibers in Cotton on the Basis of a Support Vector Machine. Mathematical and Computer Modelling, 51, 1433-1437.Kaushik, R. B. (2015). On Intuitionistic Fuzzy Divergence Measure with Application to Edge Detection. Procedia Computer Science, 70, 2-8.Liberman, M. A. (1998). Determining gravimetric bark content in cotton with machine vision. Textile Research Journal, 68(2), 94-104.Millman, M. P. (2001). Computer vision for textured yarn interlace (nip) measurements at high speeds. Mechatronics, 11(8), 1025-1038.NVIDIA, C. (2019). CUDA C Programming GUIDE. Retrieved from 9th Edition: https://docs.nvidia.com/cuda/cuda-c-programming-guide/Otsu, N. A. (1979). Threshold Selection Method from Gray-level Histograms. IEEE Transactions on Systems, Man and Cybernetic, 9(1), 62-66.Pal, N. R. (1992). Some Properties of the Exponential Entropy. Inform. Sci., 66, 119-137.Tastaswadi, P. V. (1999). Machine vision for automated visual inspection of cotton quality in textile industries using color isodiscrimination contour. Computer Industrial Engineering, 37(1-2), 347-350.Wang, X. Y. (2015). A fast image segmentation algorithm for detection of pseudo-foreign fibers in lint cotton. Comput. Electr. Eng., 46, 500-510.Xuecheng, L. (1992). Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets and Systems, 52, 305-318.Yang, W. Z. (2009). A new approach for image processing in foreign fiber detection. Computers and Electronics in Agriculture, 68(1), 68-77.Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.Zhang, H. L. (2014). Applications of computer vision techniques to cotton foreign matter inspection: A review. Computers and Electronics in Agriculture, 109, 59-70.Zhang, X. L. (2011). A fast Segmentation Method for High-Resulation Color Images of Foreign Fibers in Cotton. Computers and Electronics in Agriculture, 78, 71-79.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Elektrik Mühendisliği |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Proje Numarası | 2011/3-31YLS |
| Yayımlanma Tarihi | 29 Kasım 2019 |
| Gönderilme Tarihi | 30 Temmuz 2019 |
| Yayımlandığı Sayı | Yıl 2019Cilt: 22 - Özel Sayı |
