An extension to the Randomized Hough Transform exploiting connectivity

Finding global curve segments in an image is an important task. For such a task, 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). In this paper, a novel extension of the RHT, called the Connective Randomized Hough Transform (CRHT), is suggested to improve the RHT for line detection in complex and noisy pictures. The CRHT method combines the ability of the Hough Transform for global feature extraction with curve fitting techniques by exploiting the connectivity of local edge image points. Tests demonstrate the high speed and low memory usage of the CRHT, as compared both to the Standard Hough Transform and the basic RHT. (C) 1997 Elsevier Science B.V.