An improved method for edge detection based on interval type-2 fuzzy logic

  • Authors:

  • Patricia Melin;Olivia Mendoza;Oscar Castillo

  • Affiliations:

  • Department of Research and Graduate Studies, Tijuana Institute of Technology, Tijuana, P.O. Box 4207, Chula Vista, CA, USA;School of Engineering, University of Baja California, Tijuana, Mexico;Department of Research and Graduate Studies, Tijuana Institute of Technology, Tijuana, P.O. Box 4207, Chula Vista, CA, USA

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

In this paper, a method for edge detection in digital images based on the morphological gradient and fuzzy logic is described. A basic method for edge detection was improved using fuzzy logic. An advantage of the improved method is that there is no need of applying filtering to the image. The simulation results were obtained with a type-1 fuzzy inference system (T1FIS) and with an interval type-2 fuzzy inference system (IT2FIS) for improving the edge detection method. We show that the images obtained with fuzzy logic are better than the ones obtained with only the morphological gradient method. In particular the IT2FIS achieved the best results, because of the flexibility to model the uncertainty in the gradient values and the gray ranges for the edge images. In both TIFIS and IT2FIS the membership function parameters were obtained directly from the images; this allows application of the proposed method to images with different gray scales.