Line Detection in Grayscale Images Using Hough Transform

This article demonstrates how to detect straight lines in grayscale images using the Hough transform technique. The Hough transform is a widely-used image processing method for detecting geometric shapes (such as lines, circles, and ellipses). We will explore the application of Hough transform specifically for line detection in grayscale images. The implementation involves converting images into Hough parameter space and detecting lines through peak identification in the accumulator array. Key parameters like rho resolution (distance resolution) and theta resolution (angle resolution) can be adjusted to optimize detection accuracy. The article includes practical OpenCV implementation using functions like cv2.HoughLines() or cv2.HoughLinesP() (probabilistic Hough transform) which returns line endpoints directly. Through code examples, you'll learn pre-processing steps including edge detection using Canny operator and parameter tuning techniques for different image scenarios. By following this guide, you'll gain comprehensive understanding of Hough transform-based line detection and practical skills for implementing it using OpenCV library functions.