Supervised and unsupervised art-like classifications of binary vectors on the CNN universal machine

D. Bálya, T. Roska

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be applied very efficiently as a feature detector and also for post-processing the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can also be mapped to the CNN-UM. The designed analogic CNN algorithm is capable of classifying the extracted binary feature vectors keeping the advantages of the ART networks. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. Another CNN-UM algorithm is suggested for supervised classification. In addition to the two algorithms, a new "repair" function is proposed to reduce the number of the created classes. The presented binary feature vector classification is feasible on the existing standard CNN-UM chips.

Original languageEnglish
Title of host publicationProceedings of the 7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002
EditorsRonald Tetzlaff
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages616-623
Number of pages8
ISBN (Electronic)981238121X
DOIs
Publication statusPublished - Jan 1 2002
Event7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002 - Frankfurt, Germany
Duration: Jul 22 2002Jul 24 2002

Publication series

NameProceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications
Volume2002-January

Other

Other7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002
CountryGermany
CityFrankfurt
Period7/22/027/24/02

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Keywords

  • Cellular networks
  • Cellular neural networks
  • Computer vision
  • Detectors
  • Object detection
  • Object recognition
  • Real time systems
  • Robustness
  • Subspace constraints
  • Turing machines

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Electrical and Electronic Engineering
  • Modelling and Simulation

Cite this

Bálya, D., & Roska, T. (2002). Supervised and unsupervised art-like classifications of binary vectors on the CNN universal machine. In R. Tetzlaff (Ed.), Proceedings of the 7th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2002 (pp. 616-623). [1035103] (Proceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications; Vol. 2002-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CNNA.2002.1035103