Randomized Hough transform: Difference between revisions

Revision as of 16:59, 10 February 2013

Revision as of 23:11, 14 August 2013

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==Motivation==

Although Hough transform (HT) has been widely used in curve detection, it has two major drawbacks:<ref>L. Xu, E. Oja, and P. Kultanan, "A new curve detection method: Randomized Hough transform (RHT)", ''Pattern Recog. Lett.'' 11, 1990, 331-338.</ref> (1)First, Forfor each nonzero pixel in the image, the parameters for the existing curve and redundant ones are both accumulated during the voting procudure. (2)Second, Thethe accumulator array (or Hough space) is predefined in a heuristic way. The bettermore accuracy we needneeded, the higher parameter resolution should be defined. These two needs usually result in a large storage requirement and low speed for real applications. Therefore, RHT was brought up to tackle this problem.

==Implementation==

In comparison with HT, RHT takes advantage of the fact that some analytical curves can be fully determined by a certain number of points on the curve. For example, a straight line can be determined by two points, and an ellipse (or a circle) can be determined by three points. ToThe illustratecase theof basicellipse ideadetection ofcan RHT,be let'sused considerto illustrate the casebasic idea of ellipse detectionRHT. The whole process generally consists of three steps:

1)# Fit ellipses with randomly selected points;.

2)# Update the accumulator array and corresponding scores;.

1) Fit ellipses with randomly selected points;

3)# Output the ellipses with scores higher than some predefined threshold.

2) Update the accumulator array and corresponding scores;

3) Output the ellipses with scores higher than some predefined threshold.

===Ellipse fitting===