A COMPARISON BETWEEN THE RANDOMIZED HOUGH TRANSFORMATION AND THE TABU SEARCH ALGORITHM IN GEOMETRIC PRIMITIVE EXTRACTION
Ming Tang · Chinese Journal of Computers · 1999
The Hough transformation has been a widely used in geometric primitive extraction.It relies basically on an evidence accumulation process.Recently a new family of techniques,namely the optimization based one was proposed which resorts mainly to repeated cost function evaluation process.The randomized Hough transformation and the Tabu search algorithm are the two good representatives of the the Hough family and optimization based family respectively.Although some piecemeal works exist,a systematic comparison between these two families of techniques is unavailable in the literature.In this paper,based on a reasonable criterion,namely the expected number of random samples of minimum subset for a single successful primitive extraction,the performance of between the randomized Hough transform and the Tabu search algorithm is compared.The paper shows that the randomized Hough transformation generally outperforms the Tabu search algorithm.In particular,based on a large number of simulations and experiments with real images,it shows that with a comparable performance,the randomized Hough transformation is about twice as fast as the Tabu search algorithm for both line extraction and circle extraction.