In defense of the Hough transform
Zhaozheng Hu, Yuchen Yang, H.T. Tsui · 2002
The Hough transform has been a widely used technique for geometric primitive extraction. However, recently, a new family of techniques based on optimization, such as the genetic algorithm, the tabu search algorithm, the algorithm based on random samples of minimum subset, claimed their superiority over the Hough transform. 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 the two families of technique is compared. We show that the Hough transform generally outperforms optimization based techniques. In particular, based on a large number of simulations and experiments with real images, we show that with a comparable performance, the randomized Hough transform (RHT), a representative of Hough techniques, is about twice as fast as the random sample consensus (RANSAC), a representative of optimization based techniques, in both line extraction and circle extraction.