Randomized Hough transform: Difference between revisions

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Hough transforms are techniques for [[object detection]], a critical step in many implementations of [[computer vision]], or [[data mining]] from images. Specifically, the '''Randomized Hough transform''' is a probabilistic variant to the classical [[Hough transform]], whichand is a commonly used techniqueto for detectingdetect curves (straight line, circle, ellipse, etc.)<ref>D.H. Ballard, "Generalizing the Hough Transform to Detect Arbitrary Shapes", Pattern Recognition, Vol.13, No.2, p.111-122, 1981</ref> The basic idea of Hough transform (HT) is to implement a voting procedure for all potential curves in the image, and at the termination of the [[algorithm]], curves that do exist in the image will have relatively high voting scores. Randomized Hough transform (RHT) is different from HT in that it tries to avoid conducting the computationally expensive voting process for every nonzero pixel in the image by taking advantage of the geometric properties of analytical curves, and thus improve the time efficiency and reduce the storage requirement of the original algorithm.

{{underlinked|date=October 2012}}

'''Randomized Hough transform''' is a probabilistic variant to the classical [[Hough transform]], which is a commonly used technique for detecting curves (straight line, circle, ellipse, etc.)<ref>D.H. Ballard, "Generalizing the Hough Transform to Detect Arbitrary Shapes", Pattern Recognition, Vol.13, No.2, p.111-122, 1981</ref> The basic idea of Hough transform (HT) is to implement a voting procedure for all potential curves in the image, and at the termination of the algorithm curves that do exist in the image will have relatively high voting scores. Randomized Hough transform (RHT) is different from HT in that it tries to avoid conducting the computationally expensive voting process for every nonzero pixel in the image by taking advantage of the geometric properties of analytical curves, and thus improve the time efficiency and reduce the storage requirement of the original algorithm.

==Motivation==