The Use of Interval Type-2 Fuzzy Logic as a General Method for Edge Detection
Olivia D. Mendoza, Patricia Melín, Juan R. Castro · 2009
We describe a method for edge detection in digital images based on the morphological gradient and fuzzy logic. The goal is to improve one of the basic methods for edge detection in order to obtain a better result even without applying any filter to the image. The tests were made with a type-1 fuzzy inference system (T1FIS) and with an interval type-2 fuzzy inference system (IT2FIS). 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. we apply the same criteria and conditions, for a valid comparison of the results. Section four shows the obtained results with the three methods. The main topic in this paper is the comparison of the results obtained with the IT2FIS, applying different footprint of uncertainty (FOU).