Interval type-2 fuzzy logic for edges detection in digital images

  • Authors:

  • Olivia Mendoza;Patricia Melin;Guillermo Licea

  • Affiliations:

  • Department of Research and Graduate Studies, Universidad Autónoma de Baja California, Tijuana, Mexico;Department of Research and Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico;Department of Research and Graduate Studies, Universidad Autónoma de Baja California, Tijuana, Mexico

  • Venue:
  • International Journal of Intelligent Systems - Analysis and Design of Hybrid Intelligent Systems
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Edges detection in a digital image is the first step in an image recognition system. In this paper, we show an efficient edges detector using an interval type-2 fuzzy inference system (FIS-2). The FIS-2 uses as input the original images after applying Sobel filters and attenuation filters, then the fuzzy rules infer normalized values for the edges images, especially useful to enhance the performance of neural networks. To illustrate the results, we built frequency histograms of some images and compare the results of the FIS-2 edge's detector with the gradient magnitude method and a type-1 fuzzy inference system (FIS-1). The FIS-2 results are better than the gradient magnitude and FIS-1, because the edges preserve more detail of the original images, and the backgrounds are more homogeneous than with FIS-1 and the gradient's magnitude method. © 2009 Wiley Periodicals, Inc.