A two-scale attention model for intelligent evaluation of yarn surface qualities with computer vision

References

• ASTM. (2013). Standard test method for grading spun yarns for appearance (D2255/D2255M–09). West Conshohocken, PA: ASTM International.
   Google Scholar
• Bruce, N., & Tsotsos, J. (2006). Saliency based on information maximization. Advances in Neural Information Processing Systems, 18, 155.
   Google Scholar
• Bruce, N. D., & Tsotsos, J. K. (2009). Saliency, attention, and visual search: An information theoretic approach. Journal of Vision, 9, 5–5.10.1167/9.3.5
   PubMed Web of Science ®Google Scholar
• Chimeh, M. Y., Tehran, M. A., Latifi, M., & Mojtahedi, M. M. (2005). Characterizing bulkiness and hairiness of air-jet textured yarn using imaging techniques. The Journal of The Textile Institute, 96, 251–255.10.1533/joti.2005.0009
   Google Scholar
• Cybulska, M. (1999). Assessing yarn structure with image analysis methods1. Textile Research Journal, 69, 369–373.10.1177/004051759906900511
   Web of Science ®Google Scholar
• Fabijańska, A. (2010, April). A survey of thresholding algorithms on yarn images. In 2010 Proceedings of VIth International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH) (pp. 23–26). IEEE.
   Google Scholar
• Fabijańska, A. (2011). Yarn image segmentation using the region growing algorithm. Measurement Science and Technology, 22, 114024.10.1088/0957-0233/22/11/114024
   Web of Science ®Google Scholar
• Fabijańska, A., & Jackowska-Strumiłło, L. (2012). Image processing and analysis algorithms for yarn hairiness determination. Machine Vision and Applications, 23, 527–540.10.1007/s00138-012-0411-y
   Web of Science ®Google Scholar
• Guha, A., Amarnath, C., Pateria, S., & Mittal, R. (2010). Measurement of yarn hairiness by digital image processing. The Journal of The Textile Institute, 101, 214–222.10.1080/00405000802346412
   Web of Science ®Google Scholar
• Guler, I., & Ubeyli, E. D. (2007). Multiclass support vector machines for EEG-signals classification. IEEE Transactions on Information Technology in Biomedicine, 11, 117–126.10.1109/TITB.2006.879600
   PubMedGoogle Scholar
• Harel, J., Koch, C., & Perona, P. (2007). Graph-based visual saliency. In Advances in neural information processing systems (pp. 545–552).
   Google Scholar
• Hou, X., Harel, J., & Koch, C. (2012). Image signature: Highlighting sparse salient regions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 194–201.
   PubMed Web of Science ®Google Scholar
• Hou, X., & Zhang, L. (2007, June). Saliency detection: A spectral residual approach. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–8), 2007. CVPR’07. IEEE.
   Google Scholar
• Hou, X., & Zhang, L. (2009). Dynamic visual attention: Searching for coding length increments. In Advances in Neural Information Processing Systems (pp. 681–688).
   Google Scholar
• Itti, L., & Baldi, P. (2009). Bayesian surprise attracts human attention. Vision Research, 49, 1295–1306.10.1016/j.visres.2008.09.007
   PubMed Web of Science ®Google Scholar
• Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 1254–1259.10.1109/34.730558
   Web of Science ®Google Scholar
• Liang, Z., Xu, B., Chi, Z., & Feng, D. (2012). Intelligent characterization and evaluation of yarn surface appearance using saliency map analysis, wavelet transform and fuzzy ARTMAP neural network. Expert Systems with Applications, 39, 4201–4212.10.1016/j.eswa.2011.09.114
   Web of Science ®Google Scholar
• Liang, Z., Xu, B., Chi, Z., & Feng, D. D. (2014). Relative saliency model over multiple images with an application to yarn surface evaluation. IEEE Transactions on Cybernetics, 44, 1249–1258.10.1109/TCYB.2013.2281618
   PubMed Web of Science ®Google Scholar
• Marques, O. (2011). Practical image and video processing using MATLAB. Hoboken, NJ: John Wiley & Sons.10.1002/9781118093467
   Google Scholar
• Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62–66.10.1109/TSMC.1979.4310076
   Web of Science ®Google Scholar
• Qian, Y., Liang, Y., Li, M., Feng, G., & Shi, X. (2014). A resampling ensemble algorithm for classification of imbalance problems. Neurocomputing, 143, 57–67.10.1016/j.neucom.2014.06.021
   Web of Science ®Google Scholar
• Saville, B. P. (1999). Physical testing of textiles. Amsterdam: Elsevier.
   Google Scholar
• Semnani, D., Latifi, M., Tehran, M. A., Pourdeyhimi, B., & Merati, A. A. (2005a). Effect of yarn appearance on apparent quality of weft knitted fabric. The Journal of The Textile Institute, 96, 295–301.10.1533/joti.2005.0003
   Google Scholar
• Semnani, D., Latifi, M., Tehran, M. A., Pourdeyhimi, B., & Merati, A. A. (2005b). Development of appearance grading method of cotton yarns for various types of yarns. Research Journal of Textile and Apparel, 9, 86–93.10.1108/RJTA-09-04-2005-B009
   Google Scholar
• Semnani, D., Latifi, M., Tehran, M. A., Pourdeyhimi, B., & Merati, A. A. (2006). Grading of yarn appearance using image analysis and an artificial intelligence technique. Textile Research Journal, 76, 187–196.10.1177/0040517506056868
   Web of Science ®Google Scholar
• Seo, H. J., & Milanfar, P. (2009). Static and space-time visual saliency detection by self-resemblance. Journal of Vision, 9, 15–15.10.1167/9.12.15
   PubMed Web of Science ®Google Scholar
• Singh, D. (2015). Fundamentals of optics. Delhi: PHI Learning Pvt.
   Google Scholar
• Specht, D. F. (1988, July). Probabilistic neural networks for classification, mapping, or associative memory. In IEEE International Conference on Neural Networks (Vol. 1, No. 24, pp. 525–532).10.1109/ICNN.1988.23887
   Google Scholar
• Specht, D. F. (1990). Probabilistic neural networks. Neural Networks, 3, 109–118.10.1016/0893-6080(90)90049-Q
   Web of Science ®Google Scholar
• Uster® CLASSIMAT 5, The yarn classification system. (n.d.). Retrieved March, 2015, from https://www.uster.com/fileadmin/customer/Instruments/Brosch%C3%BCren/en_USTER_CLASSIMAT_5_web_brochure.pdf
   Google Scholar
• Wasserman, P. D. (1993). Advanced methods in neural computing.New York, NY: John Wiley & Sons .
   Google Scholar
• Xu, B. G., Murrells, C. M., & Tao, X. M. (2008). Automatic measurement and recognition of yarn snarls by digital image and signal processing methods. Textile Research Journal, 78, 439–456.
   Web of Science ®Google Scholar
• Yarn Analysis System (YAS). Retrieved September, 2014, from https://www.lawsonhemphill.com/lh-481-yarn-analysis-system.html
   Google Scholar
• Zhang, L., Tong, M. H., & Cottrell, G. W. (2009). SUNDAy: Saliency using natural statistics for dynamic analysis of scenes. In Proceedings of the 31st Annual Cognitive Science Conference (pp. 2944–2949). Cambridge, MA: AAAI Press.
   Google Scholar
• Zhang, L., Tong, M. H., Marks, T. K., Shan, H., & Cottrell, G. W. (2008). SUN: A Bayesian framework for saliency using natural statistics. Journal of Vision, 8, 32–32.10.1167/8.7.32
   PubMed Web of Science ®Google Scholar