Line Detection Based on Hough Transform

This MATLAB code utilizes the Hough Transform, a fundamental computer vision operation, to detect lines in binary images. It serves as a standard implementation demonstrating how the Hough Transform method works specifically for line detection. Technical Overview: The function employs the standard Hough Transform algorithm to detect lines in binary images. According to the Hough Transform principle, each pixel in the image space corresponds to a line in the Hough parameter space, and vice versa. The implementation uses polar coordinate representation where lines are defined by the equation: x*cos(θ) + y*sin(θ) = ρ. The top-left corner of the image serves as the origin of the polar coordinate system. Function Usage: [pdetect, tetadetect, accumulator] = houghline(Imbinary, pstep, tetastep, thresh) Input Parameters: Imbinary - A binary image where only pixels with value 1 will be processed by the HOUGHLINE function pstep - The radial step size (resolution) for the polar coordinate lines in the Hough space tetastep - The angular step size (resolution) in the polar coordinate system thresh - Detection threshold specifying the minimum number of pixels required to form a line in image space (must be ≥3, default value used if unspecified) Return Values: pdetect - Vector containing the detected line radii in the polar coordinate system tetadetect - Vector containing the detected line angles in the polar coordinate system accumulator - The accumulator array from Hough space representing vote counts for potential lines Implementation Details: The algorithm works by discretizing the parameter space (ρ, θ) and accumulating votes for each possible line equation. Edge pixels from the binary image vote for all possible lines that could pass through them, with the accumulator matrix tracking these votes. Peaks in the accumulator correspond to detected lines in the original image. Author: Amin Sarafrazi, May 5, 2004 This implementation provides a clear demonstration of the Hough Transform methodology for line detection, using parameter space discretization and peak detection to identify linear features in binary images. The code follows standard computer vision practices while maintaining educational clarity.