Fuzzy edge detector using entropy optimization

Madasu Hanmandlu*, John See, Shantaram Vasikarla

*Corresponding author for this work

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

29 Citations (Scopus)


This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier fh and the crossover point xc, is used to enhance the image. The entropy function is optimized to obtain the parameters fh and xc using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, & alpha; and & beta;. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.

Original languageEnglish
Title of host publicationInternational Conferen ON Information Technology 2004
Subtitle of host publicationCoding Computing
Number of pages6
ISBN (Print)0769521088
Publication statusPublished - 24 Aug 2004
Event2004 International Conference on Information Technology: Coding Computing - Las Vegas, NV, United States
Duration: 5 Apr 20047 Apr 2004


Conference2004 International Conference on Information Technology: Coding Computing
Abbreviated titleITCC 2004
Country/TerritoryUnited States
CityLas Vegas, NV


  • Contrast intensification operator
  • Crossover point
  • Edge detector
  • Entropy
  • Fuzzifier
  • Fuzzy image processing
  • Gaussian membership function
  • Image enhancement

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Engineering(all)


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