Incorporating Gradient Estimations in a Circle-Finding Probabilistic Hough Transform

Abstract:

In this paper we present a novel Probabilistic Hough Transform algorithm to detect circles. While other Probabilistic Hough Transforms reduce the generation of redundant evidence by sampling point-triples, the proposed algorithm achieves a much higher reduction in two ways. First, by using the gradient information, it allows point-pairs to define circles, and consequently decreases the sampling complexity from O(N 3 )to O(N 2 ). Secondly, the transformation is conditional, i.e. not all the pairs are eligible to vote. The evidence is gathered in a very sparse parameter space, so that peak recovery is performed readily. The result is high speed, increased accuracy and very low memory resources. Illustrative examples demonstrate the detection accuracy of the algorithm.