(44-1) 18 * << * >> * Русский * English * Содержание * Все выпуски

Threshold image target segmentation technology based on intelligent algorithms

Y.X. Cai 1, Y.Y. Xu 1, T.R. Zhang 1, D.D. Li 1

1 Hengshui University, Hengshui, Hebei 053000, China

 PDF, 1044 kB

DOI: 10.18287/2412-6179-CO-630

Страницы: 137-141.

Язык статьи: English.

Аннотация:
This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation.

Ключевые слова:
particle swarm optimization, thresholding, image segmentation, relative basis.

Цитирование:
Cai, Y.X. Threshold image target segmentation technology based on intelligent algorithms / Y.X. Cai, Y.Y. Xu, T.R. Zhang, D.D. Li // Компьютерная оптика. – 2020. – Т. 44, № 1. – С. 137-141. – DOI: 10.18287/2412-6179-CO-630.

Литература:
  1. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Machine Intell 2018; 40(4): 834-848.
  2. Wang B, Chen L, Cheng J. New result on maximum entropy threshold image segmentation based on P system. Optik 2018; 163: 81-85. DOI: 10.1016/j.ijleo.2018.02.062.
  3. Yang ZL, Min HQ, Luo RH. Multi-threshold image segmentation algorithm based on improved quantum-behaved particle swarm optimization. Journal of South China University of Technology 2015; 43(5): 126-131 and 138.
  4. Zhu HJ, Zhuang ZH, Zhou JL, Zhang F, Wang XJ, Wu YH. Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization. Multimedia Tools & Applications 2017; 76(6): 8951-8968.
  5. Yuan XC, Wu LS, Chen HW. Rail image segmentation based on Otsu threshold method. Optics & Precision Engineering 2016; 24(7): 1772-1781.
  6. Liu L, Yang N, Lan J, Li JJ. Image segmentation based on gray stretch and threshold algorithm. Optik 2015; 126(6): 626-629.
  7. Zhang C, Xie Y, Liu D, Wang L. Fast threshold image segmentation based on 2D fuzzy fisher and random local optimized QPSO. IEEE Trans Image Process 2017; 26(3): 1355-1362.
  8. Li YY, Jiao L, Shang R, Stolkin R. Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Information Sciences 2015; 294: 408-422.
  9. Li Y, Bai X, Jiao L, Xue Y. Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Applied Soft Computing 2017; 56(C): 345-356.
  10. Shen L, Huang X, Fan C. Double-group particle swarm optimization and its application in remote sensing image segmentation. Sensors 2018; 18(5): 1393.
  11. Liu Y, Mu C, Kou WD, Liu J. Modified particle swarm optimization-based multilevel thresholding for image segmentation. Soft Computing 2015; 19(5): 1311-1327.
  12. Liu Y, Hu KY, Zhu YL, Chen HN. Color image segmentation using multilevel thresholding-hybrid particle swarm optimization. In Book: Wang W, ed. Proceedings of the second international conference on mechatronics and automatic control. Cham, Heidelberg, New York, Dordrecht, London: Springer; 2015: 661-668.
  13. Deng MH, Li ZC, Zhu SP. The agriculture vision image segmentation algorithm based on improved quantum-behaved particle swarm optimization. Applied Mechanics & Materials 2015; 713-715: 1947-1950.
  14. Gao H, Pun CM, Kwong S. An efficient image segmentation method based on a hybrid particle swarm algorithm with learning strategy. Information Sciences 2016; 369: 500-521.
  15. Zhao J, Wang X, Zhang H, Hu J, Jian XM. The neutrosophic set and quantum-behaved particle swarm optimization algorithm of side scan sonar image segmentation. Acta Geodaetica et Cartographica Sinica 2016; 45(8): 935-942.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: ko@smr.ru ; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20