An Alternative Algorithm for Hough Transform

In digital image processing, the Hough Transform represents a fundamental algorithm renowned for its efficiency in detecting straight lines in images. Originally proposed by Paul Hough in 1962, this technique employs a clever coordinate transformation that maps lines from Cartesian space to Hough parameter space (typically using polar coordinates ρ and θ). This transformation converts the complex problem of curve detection into a more manageable straight line detection task in the parameter domain. The core implementation involves creating an accumulator array representing the parameter space, where each edge pixel votes for potential lines passing through it. Peaks in this accumulator correspond to detected lines in the original image. Beyond straight line detection, the Hough Transform's framework extends to various computer vision applications including circle detection (using three-dimensional parameter spaces for center coordinates and radius), ellipse recognition, and generalized shape detection. The algorithm's voting mechanism and parameter space optimization make it particularly valuable in industrial vision systems, autonomous navigation, and medical imaging analysis. Modern implementations often incorporate probabilistic Hough variants that randomly sample edge points to improve computational efficiency while maintaining detection accuracy.