CONNECTIVE RANDOMIZED HOUGH TRANSFORM (CRHT)

A new branch of Hough Transform algorithms, called probabilistic Hough Transforms, has been actively developed in recent years. One of the first was a new and efficient probabilistic version of the Hough Transform for curve detection, the Randomized Hough Transform (RHT). 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, a novel extension of the RHT, called the Connective Randomized Hough Transform (CRHT), is suggested to improve the RHT for complex and noisy pictures. Tests with synthetic and real-world images demonstrate the high speed and low memory usage of the CRHT, as compared both to the Standard Hough Transform and the basic RHT.