Journal of Computer and Communications

Volume 3, Issue 11 (November 2015)

ISSN Print: 2327-5219   ISSN Online: 2327-5227

Google-based Impact Factor: 1.98  Citations  

Foreign Fiber Image Segmentation Based on Maximum Entropy and Genetic Algorithm

HTML  XML Download Download as PDF (Size: 413KB)  PP. 1-7  
DOI: 10.4236/jcc.2015.311001    2,480 Downloads   3,180 Views  Citations

ABSTRACT

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.

Share and Cite:

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, 3, 1-7. doi: 10.4236/jcc.2015.311001.

Copyright © 2025 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.