MATLAB Hough Transform - Circle Detection

This document discusses using MATLAB's Hough Transform for detecting circular traffic signs in images. Recently, I've been working on traffic sign recognition that requires extracting circular traffic signs from digital images. MATLAB proves exceptionally powerful for this task - its Hough Transform implementation can not only accurately identify circles within images but also extract them as distinct entities. The algorithm employs a voting mechanism in parameter space to detect circular shapes, making it robust even when multiple circles are present in the same image, allowing for individual extraction of each detected circle. The implementation involves two key .M files: one for the main Hough circle detection routine and another for supporting functions. The code utilizes MATLAB's image processing toolbox functions like imfindcircles() or custom Hough transform implementations that work by accumulating votes in a 3D parameter space (x, y, radius). Users need to recompile these files before execution to ensure proper functionality across different MATLAB environments. The algorithm typically involves edge detection preprocessing, followed by Hough accumulation and peak detection phases to identify circle centers and radii. I genuinely appreciate MATLAB's comprehensive computational capabilities, which have significantly streamlined my computer vision workflow and provided substantial assistance in this traffic sign recognition project. The software's efficient handling of matrix operations and built-in image processing functions makes circle detection implementations both accurate and computationally efficient.