Line Detection in Grayscale Images with Fast Hough Transform Implementation

Line detection in grayscale images is crucial since straight lines represent fundamental geometric elements in computer vision applications. The Fast Hough Transform algorithm enables efficient line identification by converting image pixels from Cartesian coordinates to parameter space (θ, ρ), where peaks correspond to detected lines. Key implementation steps include gradient calculation using Sobel operators, non-maximum suppression for edge thinning, and probabilistic Hough line detection with endpoint localization through segment analysis. Parameter tuning (threshold, min-line-length, max-gap-size) allows control over detection sensitivity and accuracy. Experimental result images validate algorithm performance by comparing detected segments against ground truth data. Optimization techniques like multi-scale processing and parallel computation can enhance processing speed for real-time applications. Continuous algorithm refinement improves image analysis precision, expanding computer vision capabilities in areas like autonomous navigation and industrial inspection.