Comparative Analysis of mshEdgeGrayFT2 and Laplacian of Gaussian Method for Edge Detection in Grayscale Images

Abstract

Edge detection is a fundamental technique in image processing that can enhance the visual quality and readability of images by highlighting the boundaries and contours of objects, shapes, and features. It has many applications in various fields and domains, such as computer vision, machine learning, image analysis, pattern recognition, medical imaging, remote sensing, and art.Interval type-2 fuzzy logic is particularly useful for pattern recognition and image processing because it can manage the uncertainty in the gradient of an image and their aggregation. This uncertainty allows for the detection of edges that may be overlooked by conventional edge detection approaches.  In this paper, we analysed fuzzy logic-based algorithms that aim to enhance the conventional edge detection methods.A fuzzy logic type-2 based approach, mshEdgeGrayFT2 is utilized for detecting edges in images. We compared the results with the fuzzy logic type-1 based mshEdgeGrayFT1algorithm and the Laplacian of Gaussian Edge Detector. The work demonstrates comparison analysis of different edges identified for different fuzzy parameters set for fuzzy logic type-1 based mshEdgeGrayFT1algorithm and fuzzy logic type-2 based mshEdgeGrayFT2 algorithm.