Line Detection Using Hough Transform in MATLAB

The Hough transform serves as a fundamental feature extraction technique widely employed in image analysis, computer vision, and digital image processing applications. Its primary implementation for line detection involves two critical stages: First, the transform processes the input image to generate a parameter space (Hough space) representing all potential lines through accumulator array computation. Second, image points map to this parameter space as sinusoidal curves, where intersecting curves correspond to collinear points indicating detected lines. In MATLAB implementation, key functions include: - hough(): Creates the Hough transform matrix using Rho and Theta parameters - houghpeaks(): Identifies peak points in Hough space representing strongest lines - houghlines(): Extracts line segments based on peak values and minimum length thresholds The algorithm efficiently handles edge-detected binary images through theta resolution optimization (typically 0°-180°) and rho precision calibration. MATLAB's vectorized operations enable rapid computation of line parameters (ρ, θ) from Cartesian coordinates using the parametric representation x*cosθ + y*sinθ = ρ. This implementation supports customizable sensitivity adjustment through peak threshold and gap-filling parameters for robust line detection across various image conditions.