A new art-based neural architecture for pattern classification and image enhancement without prior knowledge

Frank Y. Shih, Jenlong Moh, Fu Chun Chang

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


The visual field is not perceived as an array of independent picture points. Instead, it is usually seen as consisting of a relatively small number of patterns. Whenever a picture is converted from one form to another, e.g. imaged, copied, scanned, transmitted, or displayed, the "quality" of the output picture may be lower than that of the input. In the absence of knowledge about how the given picture was actually degraded, it is difficult to predict in advance how effective a particular enhancement method will be. In this paper, the formulation of a new neural architecture is presented based on adaptive resonance theory (ART), for the pattern classification and image enhancement in the presence of noise without prior knowledge. The underlying theory and the improvement of the ART model are first investigated in classifying optical character patterns. Based upon the result, the two-layer ART model is incorporated into a four-layer neural network which is proposed whereby pre-established generalized enhancement templates are used as region or contour detection exemplars in order to fill in the gaps and eliminate the noise in a pattern without any prior knowledge of the image itself.

Original languageEnglish (US)
Pages (from-to)533-542
Number of pages10
JournalPattern Recognition
Issue number5
StatePublished - May 1992

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Signal Processing
  • Computer Vision and Pattern Recognition


  • Adaptive resonance theory
  • Character classification
  • Competitive learning
  • Image enhancement
  • Neural networks
  • Pattern classification


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