Implementing Hough Line Detection Using MATLAB

This article explores how to implement Hough line detection using MATLAB. Hough line detection is a fundamental computer vision technique used for detecting straight lines in digital images. The core algorithm works by transforming each point in the image space into the Hough parameter space (typically using polar coordinates), where points are mapped to an accumulator array. Each cell in this accumulator array corresponds to a potential line in the original image. By identifying peaks in the accumulator array through thresholding and local maximum detection, we can determine the most prominent lines present in the image. For implementing Hough line detection, we utilize MATLAB's programming environment, which provides powerful matrix operations and comprehensive visualization tools. The implementation specifically leverages MATLAB's built-in Hough functions, including hough() for transform computation and houghpeaks() for peak detection. We will demonstrate how to adjust critical parameters such as the Rho and Theta resolution to optimize detection accuracy and computational efficiency. The implementation process involves several preprocessing steps, including edge detection and image binarization. Edge detection algorithms (using functions like edge() with Canny or Sobel operators) help identify significant intensity transitions, classifying pixels as either edge or non-edge points. Binarization converts grayscale images into binary format using thresholding techniques, which is essential for reducing computational complexity in the Hough transform. These preprocessing stages are crucial for obtaining clean input data for the Hough transformation. In summary, this guide provides a complete workflow for implementing Hough line detection in MATLAB, covering from image preprocessing to parameter tuning and result visualization. Readers will learn to apply this powerful computer vision technique to their own projects, with practical insights into algorithm optimization and MATLAB's image processing capabilities. The implementation includes code examples for each step, demonstrating how to chain operations from edge detection through line extraction and visualization.