Abstract
In the foreign fibers cleaning process, pseudo-foreign fibers are often mistaken for foreign fibers, this result not only seriously affects the detecting precision of foreign fibers cleaning machine, but also doubles the time of cleaning up lint. As for false identification problem of pseudo-foreign fibers in cotton, this paper proposes a new approach for fast segmentation of pseudo-foreign fibers in cotton on the basis of improved genetic algorithm. This improved genetic algorithm reduced the searching range for calculating optimal threshold from 0~255 to 100~220. The calculating speed in this stage was improved more than twice in average. The fitness amendments formula is also proposed to improve genetic algorithm disadvantage, at the same time, this solved issues of "premature", and converging to global optimal solution difficultly in the traditional algorithm. The results show that the algorithm has high speed, accuracy, anti-interference and so on.
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Ji, R., Li, D., Chen, L., Yang, W.: Classification and identification of foreign fibers in cotton on the basis of support vector machine. Mathematical and Computer Modelling 51, 1433–1437 (2010)
Yang, W., Lu, S., Wang, S., Li, D.: Fast recognition of foreign fibers in cotton lint using machine vision. Mathematical and Computer Modelling 54, 877–882 (2011)
Li, D., Yang, W., Wang, S.: Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine. Computers and Electronics in Agriculture 74, 274–279 (2010)
Yang, J., Yuan, J., Wang, Z.: Study on a foreign fiber detecting system with linear CCD. Progress in Textile Science & Technology 6, 60–62 (2009)
Hu, B., Hu, B.: Difficulties of machine vision technology in the foreign fiber removed from the cotton. China Cotton Processing 5, 31–33 (2009)
General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China. GB-1103-2007, 6:1–16 (2007)
Yang, W., Li, D., Wei, X., Kang, Y., Li, F.: Toward Image Seg-mentation of Foreign Fibers in Lint. Transactions of the Chinese Society for Agricultural Machinery 40(3), 156–161 (2009)
Lu, X., Li, N., Chen, S.: Two-dimensional thresholding and genetic algorithms in image segmentations. Computer Applications and Software 12, 57–60 (2001)
Wang, L., Shen, T.: Two-dimensional entropy method based on genetic algorithm. Journal of Beijing Institute of Technology 11(2), 184–188 (2002)
Li, Z., Hou, J.: License plate image segmentation based on modified genetic algorithms. Computer Engineering and Design 26(9), 2455–2457 (2005)
Wu, Y., Wang, N., Liu, Y.: Several populations competed genetic algo-rithm and its property analysis. Journal of Northwest Sci-Tech University of Agriculture and Forestry 33(4), 154–156 (2005)
Wu, L., Shen, T., Fang, Z., Wang, F.: An image segmentation method using the entropy of histogram and genetic algorithms. Acta armamentarii 20(3), 255–258 (1999)
Li, H., Sheng, L., Chen, L., Li, G.: Image Thresholding Seg-mentation Based on 2D Maximum Entropy Principle and Improved Genetic Algorithm. Computer and Modernization 2, 34–37 (2007)
Hou, Z., Ma, S., Pei, X., Pan, X.: A Method of Marrow Cell Image Seg-mentation Based on GA. Computer Engineering & Science 28(10), 63–65 (2006)
Li, Y., Sun, W., Zhang, Z.: Chromosome Biomedicine Image Processing Based on Genetic Algorithm. Research and Exploration in Laboratory 27(5), 23–25 (2008)
Zheng, Y., Zhu, H.: Image Segmentation Approach Based on Improved Ge-netic Algorithm. Journal of Wuhan University of Technology 28(3), 436–438 (2004)
Goldberg, D.: Genetic algorithms in search, optimization and Machine Learning, 7 -10, pp. 59–308. Addison -Wesley Publishing Company, USA (1988)
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© 2012 IFIP International Federation for Information Processing
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Ge, L., Li, D., Yang, L., Yang, W. (2012). Image Segmentation of Pseudo-foreign Fibers in Cotton on the Basis of Improved Genetic Algorithm. In: Li, D., Chen, Y. (eds) Computer and Computing Technologies in Agriculture V. CCTA 2011. IFIP Advances in Information and Communication Technology, vol 369. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27278-3_55
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DOI: https://doi.org/10.1007/978-3-642-27278-3_55
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