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Randomized Hough Transform

Randomized Hough Transform

Lei Xu, Erkki Oja
Copyright: © 2009 |Pages: 8
ISBN13: 9781599048499|ISBN10: 1599048493|EISBN13: 9781599048505
DOI: 10.4018/978-1-59904-849-9.ch197
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MLA

Xu, Lei, and Erkki Oja. "Randomized Hough Transform." Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, et al., IGI Global, 2009, pp. 1343-1350. https://doi.org/10.4018/978-1-59904-849-9.ch197

APA

Xu, L. & Oja, E. (2009). Randomized Hough Transform. In J. Rabuñal Dopico, J. Dorado, & A. Pazos (Eds.), Encyclopedia of Artificial Intelligence (pp. 1343-1350). IGI Global. https://doi.org/10.4018/978-1-59904-849-9.ch197

Chicago

Xu, Lei, and Erkki Oja. "Randomized Hough Transform." In Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado, and Alejandro Pazos, 1343-1350. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-849-9.ch197

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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.

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