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Edge Detection Method Based on Nonlinear Spiking Neural Systems.

Ronghao Xian1, Rikong Lugu1, Hong Peng1

  • 1School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.

International Journal of Neural Systems
|November 3, 2022
PubMed
Summary
This summary is machine-generated.

A new nonlinear spiking neural P (NSNP) system with two outputs (NSNP-TO) was developed for image edge detection. This ED-NSNP detector framework, optimized using particle swarm optimization (PSO), shows effective performance compared to existing methods.

Keywords:
Nonlinear spiking neural P systemsedge detectionnonlinear spiking neural P system with two outputsparticle swarm optimization

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Area of Science:

  • Computational Neuroscience
  • Image Processing
  • Artificial Intelligence

Background:

  • Nonlinear spiking neural P (NSNP) systems are neural-like models inspired by the nonlinear dynamics of biological spiking neurons.
  • These systems possess a unique nonlinear spiking mechanism, differentiating them from other computational models.
  • Existing edge detection methods may not fully leverage the complex nonlinear dynamics found in biological neurons.

Purpose of the Study:

  • To introduce a novel variant of NSNP systems, termed NSNP systems with two outputs (NSNP-TO), specifically designed for image edge detection.
  • To develop an effective edge detection framework, the ED-NSNP detector, based on the proposed NSNP-TO system.
  • To enhance the detection performance of the ED-NSNP detector through parameter optimization.

Main Methods:

  • A new nonlinear spiking neural P (NSNP) system with two outputs (NSNP-TO) was designed.
  • An edge detection framework (ED-NSNP detector) was developed utilizing the NSNP-TO system.
  • Particle swarm optimization (PSO) was employed to optimize the parameters of the two convolutional kernels within the ED-NSNP detector.

Main Results:

  • The ED-NSNP detector demonstrated effective image edge detection capabilities.
  • Performance evaluation on benchmark images showed the proposed detector's availability and effectiveness.
  • Comparative analysis against seven baseline edge detection methods validated the ED-NSNP detector's superiority.

Conclusions:

  • The proposed ED-NSNP detector, based on the NSNP-TO system, offers a viable and effective solution for image edge detection.
  • The integration of nonlinear spiking mechanisms and PSO optimization contributes to improved detection performance.
  • The ED-NSNP detector represents a promising advancement in neural-inspired image processing techniques.