Hough transform and signal detection theory performance for images with additive noise

The line detection performance and sensitivity to the noise distribution of the Hough transform and two signal detection theory processors are evaluated quantitatively (using receiver operating characteristics (ROC)) and compared for images corrupted by each of several types of additive noise. The types of noise distributions considered are Gaussian, uniform, and Laplacian. The two types of signal detection theory processors considered are the optimal detector for additive, Gaussian noise and the optimal detector for additive, Laplacian noise. The performances for these noise distributions are interesting to compare because they vary widely in the thickness of the tails of their probability density functions. The Gaussian processor and the Hough transform are found to be much less sensitive to noise type than the Laplacian processor.