Image segmentation based on multi-scan constraint satisfaction neural network

Fatih Kurugollu, B. Sankur

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

A novel image segmentation method based on a constraint satisfaction neural network (CSNN) is presented. The new method uses CSNN-based relaxation but with a modified scanning scheme of the image. The pixels are visited with more distant intervals and wider neighborhoods in the first level of the algorithm. The intervals between pixels and their neighborhoods are reduced in the following stages of the algorithm. This method contributes to the formation of more regular segments rapidly and consistently. A cluster validity index to determine the number of segments is also added to complete the proposed method into a fully automatic unsupervised segmentation scheme. The results are compared quantitatively by means of a novel segmentation evaluation criterion. The results are promising.
Original languageEnglish
Pages (from-to)1553-1563
Number of pages11
JournalPattern Recognition Letters
Volume20
Issue number14
DOIs
Publication statusPublished - Dec 1999

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

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