Immune response inspired spatial-temporal target detection algorithms with CNN-UM

György Cserey, András Falus, Tamás Roska

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

In this paper we show that, similar to the nervous system and the genetic system, the immune system provides a prototype for a 'computing mechanism.' We are presenting an immune response inspired algorithmic framework for spatial-temporal target detection applications using CNN technology (IEEE Trans. Circuits Syst. II 1993; 40(3):163-173; Foundations and Applications. Cambridge University Press: Cambridge, 2002). Unlike most analogic CNN algorithms (IEEE Trans. Circuits Syst. 1988; 35(10):1257-1290; Foundations and Applications. Cambridge University Press: Cambridge, 2002) here we will detect various targets by using a plethora of templates. These algorithms can be implemented successfully only by using a computer upon which thousands of elementary, fully parallel spatial-temporal actions can be implemented in real time. In our tests the results show a statistically complete success rate, and we are presenting a special example of recognizing dynamic objects. Results from tests in a 3D virtual world with different terrain textures are also reported to demonstrate that the system can detect unknown patterns and dynamical changes in image sequences. Applications of the system include in explorer systems for terrain surveillance.

Original languageEnglish
Pages (from-to)21-47
Number of pages27
JournalInternational Journal of Circuit Theory and Applications
Volume34
Issue number1
DOIs
Publication statusPublished - Jan 1 2006

Keywords

  • Analogic algorithms
  • Artificial immune systems
  • CNN Universal Machine
  • Scellular non-linear/neural networks (CNN)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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