PROBABILISTIC AND NONPROBABILISTIC HOUGH TRANSFORMS - OVERVIEW AND COMPARISONS

A new and efficient version of the Hough transform for curve detection, the Randomized Hough Transform (RHT), has been recently suggested. The RHT selects n pixels from an edge image by random sampling to solve n parameters of a curve and then accumulates only one cell in a parameter space. In this paper, the RHT is related to other recent developments of the Hough transform. Hough transform methods are divided into two categories: probabilistic and non-probabilistic methods. An overview of these variants is given. Some novel extensions of the RHT are proposed to improve the RHT for complex and noisy images. These new versions of the RHT, called the Dynamic RHT, and the Window RHT with its variants, use local information of the edge image. They apply the RHT process to a limited neighbourhood of edge pixels. Tests in line detection with synthetic and real-world images demonstrate the high speed and low memory usage of the new extensions, as compared both to the basic RHT and other versions of the Hough transform.