Image Segmentation Combining Pulse Coupled Neural Network and Adaptive Glowworm Algorithm

Authors

  • Juan Zhu School of Mechatronic Engineering, Changchun University of Technology, Changchun 130022, China
  • Yuqing Ma School of Physics, Northeast Normal University, Changchun 130024, China
  • Jipeng Huang School of Physics, Northeast Normal University, Changchun, China
  • Lianming Wang School of Marine Science and Technology, Hainan Tropical Ocean University, Hainan, 572022, China

DOI:

https://doi.org/10.5755/j01.itc.52.2.33415

Keywords:

image segmentation;, glowworm swarm optimization algorithm;, pulse coupled neural network;, fitness function

Abstract

Image segmentation is one of the key steps of target recognition. In order to improve the accuracy of image segmentation, an image segmentation algorithm combining Pulse Coupled Neural Network(PCNN) and adaptive Glowworm Algorithm(GA) is proposed. The algorithm retains the advantages of the GA. Introduce the adaptive moving step size and the population optimal value as adjustment factors. Enhance the ability to solve the global optimal value, and takes the weighted sum of the cross entropy, information entropy and compactness of the image as the fitness function of the GA. Maintain the diversity of image features and improving the accuracy of image segmentation. Experimental results show that compared with other algorithms, the segmented image obtained by this algorithm has better visual effect and the segmentation performance has the best comprehensive performance. For the seven gray-scale images in the Berkeley segmentation dataset, the segmentation effect is improved by 10.85% compared with TDE algorithm, 9.22% compared with GA algorithm, and 22.58% compared with AUTO algorithm.

Author Biographies

Juan Zhu, School of Mechatronic Engineering, Changchun University of Technology, Changchun 130022, China

 

 

Yuqing Ma, School of Physics, Northeast Normal University, Changchun 130024, China

 

 

Jipeng Huang, School of Physics, Northeast Normal University, Changchun, China

 

 

Lianming Wang, School of Marine Science and Technology, Hainan Tropical Ocean University, Hainan, 572022, China

 

 

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Published

2023-07-15

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Section

Articles