SubpixelHought

SubpixelHought is an algorithm for detecting lines in digital images with subpixel accuracy. It is an extension of the classic Hough Transform, which is widely used for detecting shapes such as lines, circles, and ellipses. The standard Hough Transform discretizes the parameter space of the shape being detected, leading to a loss of precision. SubpixelHought addresses this limitation by employing interpolation techniques to estimate the precise location of lines between the discrete bins of the Hough parameter space.

The core idea of SubpixelHought is to refine the peak detection in the Hough accumulator. Instead of simply selecting the bin with the highest count, it analyzes the distribution of votes around the peak. By fitting a curve (often a parabola or a more complex function) to the vote counts in the neighboring bins, SubpixelHought can estimate the true peak location with much finer granularity, achieving subpixel precision. This interpolation significantly improves the accuracy of line detection, making it suitable for applications where precise measurements are critical.

Applications of SubpixelHought include computer vision tasks such as image segmentation, object recognition, and measurement. It is particularly useful in fields like industrial inspection, medical imaging, and autonomous navigation, where accurate detection and localization of features are essential for reliable operation. The computational cost of SubpixelHought is slightly higher than the standard Hough Transform due to the interpolation step, but the gain in accuracy often justifies the increased processing time.