Fuzzy Modeling of Image Edge Detection Based on Image Complexity

  • Mario I. Chacon M.
  • Luis Aguilar D.
  • Abdi Delgado S.


This paper presents a novel modeling technique of image edge detection based on the complexity of images. The method is composed of an edge level detector and an adaptive edge level analysis. Edge level detection is related to how much attention a person needs to use to detect an edge. The edge level stage is based on the analysis of gradient information of images that is considered as fuzzy information. The adaptive edge detector section defines which edges will be shown according to the complexity of the image. Complexity is considered as a subjective characteristic that is represented by a fuzzy interpretation of the percentage of edges in an image. The edges that appear in the final result are selected through an integration of edge levels using fuzzy rules. Results of the new algorithm indicate that the behavior of the method is adaptive according to the complexity of the image as stated in our theory and they turn to be better than those obtained with conventional methods as shown in the paper.