Multi-object Segmentation Based on Improved Pulse Coupled Neural Network


  •  Dansong Cheng    
  •  Xianglong Tang    
  •  Jiafeng Liu    
  •  Xiaofang Liu    

Abstract

This paper introduces an approach for image segmentation by using pulse coupled neural network (PCNN), based on the phenomena of synchronous pulse bursts in the animal visual cortexes. The synchronous bursts of neurons with different input were generated in the proposed PCNN model to realize the multi-object segmentation. The criterion to automatically choose the dominant parameter (the linking strength ?), which determines the synchronous-burst stimulus range, was described in order to stimulate its application in automatic image segmentation. Segmentations on several types of image are implemented with the proposed method and the experimental results demonstrate its validity.


This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1913-8989
  • Issn(Onlne): 1913-8997
  • Started: 2008
  • Frequency: quarterly

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(The data was calculated based on Google Scholar Citations)

Google-based Impact Factor (2018): 18.20

h-index (January 2018): 23

i10-index (January 2018): 90

h5-index (January 2018): 11

h5-median(January 2018):17

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