SUSAN Edge Detection MATLAB Implementation

This document provides a comprehensive technical description of the SUSAN edge detection algorithm implementation. Below you will find the MATLAB implementation code for performing edge detection on static images. The effectiveness of this algorithm may vary depending on the specific image characteristics and content. Edge detection represents a fundamental image processing technique aimed at identifying boundaries and contours within digital images. The SUSAN (Smallest Univalue Segment Assimilating Nucleus) edge detection algorithm employs a robust approach that analyzes local pixel intensity variations to determine edge presence. The core algorithm operates by comparing pixel intensities within a circular mask against a central nucleus pixel, using a brightness threshold to classify edge points. Key implementation aspects include: - Circular mask generation for neighborhood analysis - Intensity comparison threshold configuration - Nucleus similarity calculations - Edge response magnitude computation This MATLAB implementation allows researchers and developers to effectively extract edge structures from images, facilitating subsequent analysis and processing tasks. The algorithm finds extensive applications across computer vision systems, medical imaging, industrial inspection, and various image processing domains. Through this SUSAN edge detection MATLAB code, users can gain deeper insights into image boundary characteristics and perform advanced image analysis operations.