In machine-vision-based systems
for detecting foreign fibers, due to the background of the cotton layer has the
absolute advantage in the whole image, while the foreign fiber only account for
a very small part, and what’s more, the brightness and contrast of the image
are all poor. Using the traditional image segmentation method, the segmentation
results are very poor. By adopting the maximum entropy and genetic algorithm,
the maximum entropy function was used as the fitness function of genetic
algorithm. Through continuous optimization, the optimal segmentation threshold
is determined. Experimental results prove that the image segmentation of this
paper not only fast and accurate, but also has strong adaptability.
Cite this paper
Chen, L. , Chen, X. , Wang, S. , Yang, W. and Lu, S. (2015) Foreign Fiber Image Segmentation Based on Maximum Entropy and Genetic Algorithm. Journal of Computer and Communications
, 1-7. doi: 10.4236/jcc.2015.311001
 Yang, M.H. (2014) Hazards and Prevention of Foreign Fiber in Cotton. Jiangsu Textile, 4, 44-46. (In Chinese with English Abstract)
 Zhang, Y.J. (2013) Image Engineering (II) Image Analysis. Tsinghua University Press, Beijing.
 He, Z.J. and Wang, H.F. (2010) Chinese Word Sense Disambiguation Based on Maximum Entropy Model with Feature Selection. Journal of Software, 21, 1287-1295. http://dx.doi.org/10.3724/SP.J.1001.2010.03591
 Wu, Y.Q. and Zhang, X.J. (2011) Two-Dimensional Symmetric Cross-Entropy Image Thresholding. Journal of Image and Graphics, 16, 1393-1401.
 Wu, Y.Q., Meng, T.L. and Wu, S.H. (2015) Research Progress of Image Threshold Methods in Recent 20 Years (1994- 2014). Journal of Data Acquisition and Processing, 30, 1-23.
 Ou, P. and He, D. (2011) 2-D Maximum Entropy Method of Image Segmentation Based on Genetic Algorithm. Journal of Computer Simulation, 1, 294-297.
 Guo, M.S. and Liu, B.H. (2008) 2-D Maximum Entropy Method in Image Segmentation Based on Chaos Genetic Algorithm. Computer Technology and Development, 18, 101-104.
 Cao, J.N. (2012) Re-view on Image Segmentation Based on Entropy. Pattern Recognition and Artificial Intelligence, 25, 958-959.
 Zhang, C.Q., Zheng, J.G. and Qian, J. (2011) Comparison of Coding Schemes for Genetic Algorithms. Application Research of Computers, 28, 819-822.
 Cao, D.Y. and Cheng, J.X. (2010) A Genetic Algorithm Based on Modified Selection Operator and Crossover Operator. Computer Technology and Development, 20, 44-51.
 Yang, W.Z., Li, D.L. and Zhu, L. (2010) An Improved Genetic Algorithm for Optimal Feature Subset Selection from Multi-Character Feature Set. Journal of Agricultural Machinery, 38, 2733-2740.