Fuzzy adaptive edge definition based on the complexity of the image

This paper presents a novel fuzzy method to define edges of interest in images under analysis. It is an adaptive method that automatically 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. These fuzzy rules were derived from the analysis of edge level components over a set of images. Results over these images indicate that the behavior of the method is adaptive according to the complexity of the image as it was stated in our theory and they turn to be better than other methods as shown in the paper. These results also prove that integration of information, edges, according to what a vision system sees can be used for subsequence stages of analysis making a vision system more robust.