Hough Transform for Line Extraction with MATLAB Implementation

While the Hough transform-based MATLAB program can effectively detect straight lines in images, several optimization approaches can be implemented to improve line detection performance in practical applications. For instance, incorporating edge detection algorithms like Canny or Sobel operators as preprocessing step can enhance results by first extracting edge information from the image, followed by line fitting techniques on the detected edges for more accurate line identification. The implementation typically involves using MATLAB's hough() function for transform calculation and houghpeaks() for peak detection in parameter space. Additionally, developers can employ multiple parameter configurations and filtering techniques to adapt to different image types. This includes adjusting the Rho and Theta resolution parameters in the Hough transform, implementing Gaussian filtering for noise reduction, and using thresholding methods in houghpeaks() function to control sensitivity. These adaptations significantly improve the robustness and accuracy of line detection across various image conditions. When utilizing the Hough transform MATLAB program, combining these methods through systematic code implementation - such as creating parameter tuning loops, integrating edge detection workflows, and applying post-processing validation checks - can optimize the line detection results. The program structure typically involves image preprocessing, Hough space computation, peak detection, and line segment extraction using houghlines() function with appropriate angle and gap thresholds.