An Expert System Based on Type-۱ Fuzzy Logic and Digital Image Processing for Knowledge Based Edge and Contour Detection

In computer vision, contour/edge detection is a crucial phenomenon. Edge detection is an important step in contour detection, which is helpful in the identification of important data. The accuracy of the edge detection process is heavily dependent on the edge localization and orientation. Due to their versatility, in recent past, soft computing approaches are considered as effective edge detection strategies. Broadly, edge detection accuracy is deeply hampered by weak and dull edges. In recent works, edge detection based on fuzzy logic (FL) was proposed, and image edges were improved using guided filtering. However, guided image filtering does not take into account for the local features of an image. To include local features of an image for edge detection, an improved version i.e., an offset enable sharpening-guided filter is used in this paper, and FL is used for edge detection. Figure of merit (FoM) and F-score are used to evaluate the method's accuracy. Using visual representations and performance metrics, the results are compared with those from cutting-edge techniques.

کلیدواژه ها:

Edge detection ، Fuzzy logic ، F-score