Hough Transform Implementation with MATLAB Source Code

In this text, I will provide additional information about the Hough transform along with MATLAB source code to implement this algorithm.

The Hough transform is a technique used for detecting geometric shapes in images. It is commonly employed for detecting lines, circles, and other shapes. By mapping image pixels to a parameter space, the Hough transform represents geometric shapes as peaks in the parameter space. This enables us to extract information about the position and orientation of geometric shapes from images.

Below is a comprehensive MATLAB source code example demonstrating Hough transform implementation:

% Read input image image = imread('image.jpg');

% Convert to grayscale using rgb2gray function grayImage = rgb2gray(image);

% Perform edge detection using Canny algorithm edgeImage = edge(grayImage, 'canny');

% Apply Hough transform - returns accumulator matrix H, angle vector theta, and distance vector rho [H, theta, rho] = hough(edgeImage);

% Detect peaks in the Hough accumulator matrix using houghpeaks function peaks = houghpeaks(H);

% Extract line segments from the detected peaks using houghlines function lines = houghlines(edgeImage, theta, rho, peaks);

% Display original image with overlaid detected lines figure, imshow(image), hold on

% Plot each detected line segment with green color and thickness of 2 pixels for i = 1:length(lines) xy = [lines(i).point1; lines(i).point2]; plot(xy(:,1), xy(:,2), 'LineWidth', 2, 'Color', 'green'); end

This implementation follows the standard Hough transform workflow: image preprocessing, edge detection, parameter space mapping, peak detection, and line segment extraction. The algorithm efficiently converts image space features to parameter space representation where shape detection becomes a peak-finding problem.

I hope this information and source code helps you better understand the Hough transform algorithm and its implementation in MATLAB.