Given a pre-specified threshold, an accumulator in an array is picked if it receives votes larger than the threshold, without considering any neighborhood. Finding a local maximum means to find an accumulator with its votes larger than those of accumulators located in its neighborhood area.
Published in Chapter:
Randomized Hough Transform
Lei Xu (Chinese University of Hong Kong & Peking University, China) and Erkki Oja (Helsinki University of Technology, Finland)
Copyright: © 2009
|Pages: 8
DOI: 10.4018/978-1-59904-849-9.ch197
Abstract
Proposed in 1962, the Hough transform (HT) has been widely applied and investigated for detecting curves, shapes, and motions in the fields of image processing and computer vision. However, the HT has several shortcomings, including high computational cost, low detection accuracy, vulnerability to noise, and possibility of missing objects. Many efforts target at solving some of the problems for decades, while the key idea remains more or less the same. Proposed in 1989 and further developed thereafter, the Randomized Hough Transform (RHT) manages to considerably overcome these shortcomings via innovations on the fundamental mechanisms, with random sampling in place of pixel scanning, converging mapping in place of diverging mapping, and dynamic storage in place of accumulation array. This article will provides an overview on advances and applications of RHT in the past one and half decades.