HOUGH Line Detection Design Program

In this design program, we implement HOUGH line detection functionality through the following key steps: (1) Reading input images that should be binary images (black-white format) for processing; (2) Performing Hough transform and outputting the transformation results; (3) Detecting and outputting images containing identified specific lines.

To elaborate on these steps with implementation details: First, we load the target image using image reading functions (e.g., imread() in MATLAB/OpenCV), converting it to binary format through thresholding operations if necessary. The Hough transform algorithm then processes the image by mapping edge points from Cartesian coordinates to Hough parameter space, typically using functions like HoughLines() or similar implementations that accumulate votes in parameter space. For code implementation, we utilize peak detection in the Hough space to identify dominant lines, followed by line segment reconstruction using inverse transformation. Finally, the program superimposes detected lines onto the original image using line drawing functions (e.g., line() or plot()), highlighting the identified linear features.

This design program provides efficient HOUGH line detection capabilities, enabling extraction of linear information from images. The methodology proves valuable for various computer vision tasks including edge detection, object recognition, and structural analysis. The implementation includes parameter tuning for theta and rho resolution to balance detection accuracy and computational efficiency.

This detailed explanation aims to enhance understanding of the program's functionality and implementation approach for effective usage in image processing applications.