A machine vision system to estimate cotton fiber maturity from longitudinal view using a transfer learning approach
- 377 Downloads
- 3 Citations
Abstract
This paper describes a proposed machine vision system developed to acquire longitudinal images of complete cotton fibers and then estimate their average maturity using image and pattern analysis. Maturity is important to the cotton industry because it relates to fiber’s propensity to break when submitted to mechanical stress and it influences the quality of the goods produced from it (yarns and fabrics). The proposed system is novel because it estimates maturity indirectly from fibers’ longitudinal views using auxiliary training data generated from fibers’ cross-sectional views. It uses the transfer learning framework to reconcile the distribution differences between the two views before traditional machine learning algorithms are applied to learn a suitable model for cotton fiber maturity imaged longitudinally. In addition, the proposed system is more descriptive than commercially available systems currently used in the cotton industry because it estimates not only the average maturity of a complete cotton fiber, but also the maturity variations along the fiber from end to end. Validation studies performed show that the transfer learning approach is a practical and promising way to train our system.
Keywords
Cotton maturity estimation Transfer learning Feature-based domain adaptationNotes
Acknowledgments
This work is supported by a grant from Cotton Incorporated. We also thank the students Devashish Deshpande and Bosco DeSouza for their assistance in imaging cotton fibers using the proposed system.
References
- 1.Wang, H., Mao, C., Sari-Sarraf, H., Hequet, E.F.: Accurate length measurement of multiple cotton fibers. J. Electron. Imaging 17(3), 031110 (2008)Google Scholar
- 2.Shahriar, M.: Machine Vision System for Quantification of Cotton Fiber Length and Maturity. Texas Tech University, Lubbock (2008)Google Scholar
- 3.Hequet, E., Wyatt, B., Abidi, N., Thibodeaux, D.P.: Creation of a set of reference material for cotton fiber maturity measurements. Text. Res. J. 76(7), 576–586 (2006)CrossRefGoogle Scholar
- 4.AATCC-20A-Section-14: AATCC 20A-2008 Fiber Analysis: Quantitative. American Association of Textile Chemists and Colorists Research, Triangle Park (2008)Google Scholar
- 5.Xu, B., Huang, Y.: Image analysis for cotton fibers, part II: cross sectional measurements. Text. Res. J. 74, 409–416 (2004)CrossRefGoogle Scholar
- 6.Frydrych, I., Raczynska, M., Cekus, Z.: Measurement of cotton fineness and maturity by different methods. Fibres Text. East. Eur. 18(6), 54–59 (2010)Google Scholar
- 7.Long, R.L., Bange, M.P., Gordon, S.G., Constable, G.A.: Measuring the maturity of developing cotton fibers using an automated polarized light microscopy technique. Text. Res. J. 80(5), 463–471 (2010)CrossRefGoogle Scholar
- 8.Schwarz, E.R., Hotte, G.H.: Micro-determination of cotton fiber maturity in polarized light. Text. Res. J. 5(8), 370–376 (1935)CrossRefGoogle Scholar
- 9.ASTM-D1442: D1442–00 (2000): Standard test method for maturity of cotton fibers (sodium hydroxide swelling and polarized light procedures). In: American Society for Testing and Materials Designation, pp. 354–359 (2000)Google Scholar
- 10.Rodgers, J., Delhom, C., Fortier, C., Thibodeaux, D.: Rapid measurement of cotton fiber maturity and fineness by image analysis-microscopy using the Cottonscope\(^{\textregistered }\) . Text. Res. J. 82(3), 259–271 (2012)Google Scholar
- 11.Abbot, A.M., Hequet, E., Higgerson, G., Lucas, S., Naylor, G., Purmalis, M., Thibodeaux, D.: Performance of the CottonscanTM instrument for measuring the average fiber linear density (fineness) of cotton lint samples. Text. Res. J. 81(1), 94–100 (2010)CrossRefGoogle Scholar
- 12.Abbot, A.M., Higgerson, G., Lucas, S., Naylor, G.: An upgraded CottonscanTM instrument for measuring the average fiber linear density (fineness) of cotton lint samples. Text. Res. J. 81(7), 683–689 (2011)CrossRefGoogle Scholar
- 13.Lord, E.: Airflow through plugs of textile fibers. Part 2—the micronaire test. J. Text. Inst. 47, T16–T30 (1956)Google Scholar
- 14.Zhang, Z., Su, Y., Lu, C.: A novel method to assess cotton fiber qualities based on Fraunhofer diffraction. Paper presented at the 2nd International Asia Conference on Informatics in Control, Automation and RoboticsGoogle Scholar
- 15.Adedoyin, A.A., Li, C., Toews, M.D.: Characterization of single cotton fibers using a laser diffraction system. Text. Res. J. 81(4), 355–367 (2010)CrossRefGoogle Scholar
- 16.Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
- 17.Zhong, E., Fan, W., Peng, J., Zhang, K., Ren, J., Turaga, D., Verscheure, O.: Cross domain distribution adaptation via kernel mapping. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1027–1036. ACM, New York (2009)Google Scholar
- 18.Shahriar, M., Scott-Fleming, I., Sari-Sarraf, H., Hequet, E.F.: Training a new cotton imaging system via a transfer learning approach. Paper Presented at the The International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las VegasGoogle Scholar
- 19.Shahriar, M., Sari-Sarraf, H., Hequet, E.: Feature-based transfer learning to train a novel cotton imaging system. Paper Presented at the IEEE SSIAI, Sante Fe, New MexicoGoogle Scholar
- 20.Huang, Y., Xu, B.: Image analysis for cotton fibers, part I: longitudinal measurements. Text. Res. J. 72, 713–720 (2002)CrossRefGoogle Scholar
- 21.Thibodeaux, D., Evans, J.: Cotton fiber maturity by image analysis. Text. Res. J. 56, 130–139 (1986)CrossRefGoogle Scholar
- 22.Duckett, K., Cheng, C.C.: The detection of cotton fiber convolutions by the reflections of light. Text. Res. J. 42, 263–268 (1972)CrossRefGoogle Scholar
- 23.Han, Y., Cho, Y.J., Lambert, W., Bragg, C.: Identification and measurement of convolutions in cotton fibers using image analysis. Artif. Intell. Rev. 12, 201–211 (1998)CrossRefGoogle Scholar
- 24.Morlier, O.W., Orr, R.S., Grant, J.N.: The relation of length to other physical properties of cotton fibers. Text. Res. J. 21, 6–13 (1951)CrossRefGoogle Scholar
- 25.Pillay, K.P.R., Shankaranarayana, K.S.: Variation in the properties of cotton fibers with length. Text. Res. J. 31, 515–524 (1961)CrossRefGoogle Scholar
- 26.Fiori, L.A., Louis, G.L., Sands, J.E.: Blending cottons differing widely in maturity: part I: effect on properties of single yarns. Text. Res. J. 29, 706–716 (1959)Google Scholar
- 27.Sands, J.E., Louis, G.L., Tallant, J.D.: Linear densities of fibers in selected length groups of 42 domestic and foreign cottons. Text. Res. J. 30, 619–620 (1960)CrossRefGoogle Scholar
- 28.Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
- 29.Otsu, N.: A threshold selection method from gray-level histograms. Paper presented at the IEEE transactions on systems, man, and cyberneticsGoogle Scholar
- 30.Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Pearson Prentice Hall, New Jersey (2004)Google Scholar
- 31.Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. SIGMOD Rec. 29(2), 93–104 (2000). doi: 10.1145/335191.335388 CrossRefGoogle Scholar
- 32.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)MATHGoogle Scholar
- 33.Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Comput. 12(10), 2385–2404 (2000)CrossRefGoogle Scholar
- 34.Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Muller, K.-R.: Fisher discriminant analysis with kernels. Neural Netw. Signal Process. IX, 41–48 (1999)Google Scholar
- 35.Perez-Cruz, F.: Kullback–Leibler divergence estimation of continuous distributions. In: IEEE International Symposium on Information Theory, pp. 1666–1670. ISIT 2008, 6–11 July 2008 (2008)Google Scholar
- 36.Kirkpatrick, S., Jr., C.D.G., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)Google Scholar
- 37.Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, New York (2010)Google Scholar
- 38.Fox, J.: Multiple and generalized nonparametric regression. In: Sage University Papers Series on Quantitative Applications in the Social Sciences, Thousand Oaks (2000)Google Scholar
- 39.Kutner, M.H.: Applied Linear Statistical Models, 5th edn. McGraw-Hill, Irwin (2004)Google Scholar
- 40.Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981). doi: 10.1145/358669.358692