Hough Transform for Line Detection

In "Digital Image Processing" edited by Gonzalez, the Hough Transform is an effective method for detecting lines in digital images. The algorithm converts pixel points from Cartesian coordinates into lines in parameter space (typically using ρ-θ representation), transforming line detection into a peak-finding problem in the parameter space accumulator array. This approach involves creating an accumulator matrix where each bin corresponds to a specific (ρ, θ) pair. For each edge pixel in the input image, the algorithm calculates ρ = x·cosθ + y·sinθ for all possible θ values, incrementing corresponding accumulator bins. The highest values in the accumulator then indicate the most prominent lines in the image. Key implementation steps include: 1. Edge detection preprocessing (using Canny or Sobel operators) 2. Parameter space discretization and accumulator initialization 3. Voting procedure for all edge pixels 4. Peak detection using thresholding or local maxima search 5. Conversion of parameters back to line endpoints Due to its robustness against noise and discontinuous edges, Hough Transform is widely applied in computer vision and image processing applications. For deeper understanding, it's recommended to study relevant literature and implement Hough-based programs using libraries like OpenCV (cv2.HoughLines()) or MATLAB's hough function. Practical implementation enhances both theoretical comprehension and programming skills in image analysis.