A Comparative Study of Fuzzy Logic Type-1, Type-2 and Gradient-Based Method for Edge Detection in RGB Images

Abstract

Edge detection is widely used in image processing to simplify the images by reducing data. It emphasizes essential features and thereby enhances data processing speed. Edge detection techniques are widely used in the fields of Medical Imaging, Vehicle Detection, Fingerprint Recognition, Robotic vision, Steganography, Feature extraction, security etc. This research paper presents a comparative analysis of different edge detection techniques based on gradient fuzzy Type-1 and fuzzy Type-2. The researcher has experimentally evaluated the edges detected using Prewitt operator based on Gradients, mshEdgeRGB_T1 algorithm based on Fuzzy Type-1 Logic and mshEdgeRGB_T2 algorithm based on Fuzzy Type-2 Logic. The results reveal that edge pixels detected by using algorithms based on Fuzzy Logic are more in comparison to Gradient based Prewitt operator. The paper also shows a comparative analysis of edges detected by algorithms based on Fuzzy Logic for different ranges of membership values of pixels in fuzzy edge set.