An edge detection algorithm using multi-state adalines

Joon K. Paik, James C. Brailean, Aggelos K. Katsaggelos*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


An edge detection algorithm using multi-state adaptive linear neurons (ADALINES) is presented. Although the tri-state ADALINE is only considered in this work, general multi-state input vectors with extreme values are shown to be linearly separable from the rest of the vectors with the same dimension. The input state of each ADALINE is defined using the local mean in a predefined mask. In addition to the binary input states ± 1, the 0 input state is introduced for controlling the noise effect. If the input pattern matches one of the predefined edge patterns, the corresponding pixel is detected as an edge pixel. Experimental results are shown where the proposed detector is compared with both the Canny and LOG edge detectors.

Original languageEnglish (US)
Pages (from-to)1495-1504
Number of pages10
JournalPattern Recognition
Issue number12
StatePublished - Dec 1992


  • Edge detection
  • Linear neural networks
  • Pattern recognition

ASJC Scopus subject areas

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


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