본문 바로가기
  • Home

Enhanced Binarization Method using Fuzzy Membership Function

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2005, 10(1), pp.67-72
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Kwang Baek Kim ORD ID 1 Kim, Young-Ju 1

1신라대학교

Candidate

ABSTRACT

Most of image binarization algorithms analyzes the intensity distribution using the histogram for the determination of threshold value. When the intensity difference between the foreground object and the background is great, the histogram shows the tendency to be bimodal and the selection of the histogram valley as the threshold value shows the good result. On the other side, when the intensity difference is not great and the histogram doesn't show the bimodal property, the histogram analysis doesn't support the selection of the proper threshold value. This paper proposed the novel binarization method that applies the fuzzy membership function to each color value on the RGB color model and, by using the operation results, separates the features having the great readability from the background. The proposed method prevents the loss of information incurred by the gray scale conversion by using the RGB color model and extracts effectively the readable features by using the fuzzy inference. Compared with the traditional binarization methods, the proposed method is able to remove the majority of noise areas and show the improved results on the image of transport containers, etc.

Citation status

* References for papers published after 2022 are currently being built.