Automated image segmentation based on pulse coupled neural network with partide swarm optimization and comprehensive evaluation

2018 ◽  
Vol 26 (4) ◽  
pp. 962-970 ◽  
Author(s):  
张坤华 ZHANG Kun-hua ◽  
谭志恒 TAN Zhi-heng ◽  
李斌 LI bin
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Comprehensive Evaluation ◽  
Swarm Optimization ◽  
Pulse Coupled Neural Network

scholarly journals Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

2019 ◽  
Vol 11 (9) ◽  
pp. 1046 ◽  
Author(s):  
Heming Jia ◽  
Zhikai Xing ◽  
Wenlong Song
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Optimization Algorithm ◽  
Oil Pollution ◽  
Three Dimensional ◽  
Oil Field ◽  
Segmentation Method ◽  
Pulse Coupled Neural Network ◽  
Aerial Vehicle ◽  
Pollution Area

This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.

PCNN Based Welding Seam Image Segmentation Algorithm

2012 ◽  
Vol 155-156 ◽  
pp. 861-866 ◽  
Author(s):  
Bei Ji Zou ◽  
Hao Yu Zhou ◽  
Zai Liang Chen ◽  
Hao Chen ◽  
Guo Jiang Xin
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
Spatial Proximity ◽  
Variance Ratio ◽  
Segmentation Method ◽  
Welding Seam ◽  
Pulse Coupled Neural Network ◽  
Image Segmentation Algorithm

A new welding seam image segmentation method based on pulse-coupled neural network (PCNN) is presented in this paper. The method segments image by utilizing PCNN’s specific feature that the fire of one neuron can capture firing of its adjacent neurons due to their spatial proximity and intensity similarity. The method can automatically confirm the best iteration times by comparing the maximum of variance ratio and get the best segmentation results. Experimental results show that the proposed method has good performance in both results and execution efficiency.

Particle Swarm Optimization for Automatic Parameters Determination of Pulse Coupled Neural Network

2011 ◽  
Vol 6 (8) ◽  
Author(s):  
Xinzheng Xu ◽  
Shifei Ding ◽  
Zhongzhi Shi ◽  
Hong Zhu ◽  
Zuopeng Zhao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Pulse Coupled Neural Network
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