The Agriculture Vision Image Segmentation Algorithm Based on Improved Quantum-Behaved Particle Swarm Optimization

Ming Hui Deng; Zhan Cheng Li; Shao Peng Zhu

doi:10.4028/www.scientific.net/AMM.713-715.1947

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 713-715The Agriculture Vision Image Segmentation...

The Agriculture Vision Image Segmentation Algorithm Based on Improved Quantum-Behaved Particle Swarm Optimization

Abstract:

Image segmentation and feature extraction are the premise for machine vision system to analyze and identify the image. Threshold image segmentation algorithm according to the method of two dimension threshold has a lot of calculation in calculating the threshold, and the minimum error threshold method can not use the spatial information of image. This paper presents an improved quantum-behaved particle swarm optimization based on the night segmentation and feature extraction technology. This paper introduces the QPSO algorithm based on multi group and multi stage improvement. The QPSO optimizing algorithm gradually approaches the global optimum threshold value to achieve better convergence and stability. An algorithm of vision image segmentation and feature extraction based on improved quantum-behaved particle swarm optimization is designed. Experimental results show that the optimization process of this algorithm has less control parameters and faster convergence speed.

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Periodical:

Applied Mechanics and Materials (Volumes 713-715)

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1947-1950

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https://doi.org/10.4028/www.scientific.net/AMM.713-715.1947

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Online since:

January 2015

Authors:

Ming Hui Deng*, Zhan Cheng Li, Shao Peng Zhu

Keywords:

Image Segmentation, Machine Vision, Quantum-Behaved Particle Swarm Optimization

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References

[1] J. Sun et al: Parameter Selection of Quantum-behaved Particle Optimization [C]. ICNC 2005, LNCS 3612, p.543 – 552, 2005. © Springer-Verlag Berlin Heidelberg (2005).

Google Scholar

[2] Sun, J., Feng B. ，and Xu W.B. Particle Swarm Optimization with Particles Having Quantum Behavior [C]. Proceedings of 2004 Congress on Evolutionary Computation, 2004: 325-331.

DOI: 10.1109/cec.2004.1330875

Google Scholar

[3] Wenbo Xu and Jun Sun. Adaptive Parameter Selection of Quantum-Behaved Particle Swarm Optimization on Global Level [C]. ICIC 2005. 2005(Part I): 420-428.

DOI: 10.1109/icsmc.2005.1571614

Google Scholar

[4] Pal N R, Pal S K. A review on image segmentation techniques [J]. Pattern Recognition, 1993, 26: 1277-1294.

DOI: 10.1016/0031-3203(93)90135-j

Google Scholar

[5] J. Sun, W.B. Xu, B. Feng, A global search strategy of quantum-behaved particle swarm optimization[J]. IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, 2004, p.111–116.

DOI: 10.1109/iccis.2004.1460396

Google Scholar

[6] M.L. Xi, J. Sun, W.B. Xu, An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position [J]. Appl. Math. Comput. 205 (2008) 751–759.

DOI: 10.1016/j.amc.2008.05.135

Google Scholar

[7] Zhang N. ，Chaisattapagon C. Effective criteria for weed identification in wheat field using machine vision [J]. Transactions of the ASAE，1995，38(3)：135-139.

DOI: 10.13031/2013.27914

Google Scholar

[8] Sogaard H.T. ，Olsen H. J, Determination of crop rows by image analysis without segmentation. Computers and Electronics in Agriculture[J]. 2003(38): 141-158.

DOI: 10.1016/s0168-1699(02)00140-0

Google Scholar