Abstract
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 by experimental tests in line detection. Hough Transform methods are divided into two categories: probabilistic and non-probablistic methods. Four novel extensions of the RHT are proposed to improve the RHT for complex and noisy images. These apply the RHT process to a limited neighborhood of edge pixels. Tests 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.
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