Moving object extraction using multi-tiered pulse-coupled neural network

Jun Chen, Kosei Ishimura, Mitsuo Wada

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

A novel method for extraction of moving objects in an image sequence using multi-tiered Pulse-Coupled Neural Network (PCNN) is presented in this paper. PCNN is a biologically-inspired model which shows highly applicable in various image processing applications, including image segmentation, contour detection, etc. In order to adapt PCNN for moving object extraction, the multi-tiered PCNN model is proposed. This new PCNN model is called E-PCNN, since excitatory term and external linking are its two features. The architecture and algorithm of E-PCNN are presented in detail. It is shown that E-PCNN outweighs the commonly used inter-frame difference algorithm, having three main advantages: utilization of multiple color information, parameter robustness and robustness against noise.

Original languageEnglish
Pages73-78
Number of pages6
Publication statusPublished - 2004 Dec 1
Externally publishedYes
EventSICE Annual Conference 2004 - Sapporo, Japan
Duration: 2004 Aug 42004 Aug 6

Conference

ConferenceSICE Annual Conference 2004
CountryJapan
CitySapporo
Period04/8/404/8/6

Keywords

  • E-PCNN
  • Multi-tiered
  • Pulse-Coupled Neural Networks
  • Robust motion detection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Chen, J., Ishimura, K., & Wada, M. (2004). Moving object extraction using multi-tiered pulse-coupled neural network. 73-78. Paper presented at SICE Annual Conference 2004, Sapporo, Japan.