A sparse structure for fast circle detection

Abstract In the paper, we present a circle detector that achieves the state-of-art performance in almost every type of image. The detector represents each circle instance by a set of equally distributed arcs and searches for the same number of edge points to cover these arcs. The new formulation leads to the voting in minimizing/maximizing way which is different from the typical accumulative way adopted by Hough transform. From the formulation, circle detection is then decomposed into radius-dependent and -independent part. The calculation of independent part is computationally expensive but shared by different radii. This decomposition gets rid of the redundant computation in handling multiple radii and therefore speeds up the detection process. Originated from the sparse nature of independent part, we design a sparse structure for its batch computation which is fulfilled in just one sweep of the edge points. Circle detector based on this sparse structure is then proposed which achieves the comparable time complexity as the algorithm based on Hough transform using 2D accumulator array. For testing, we created an information-rich dataset with images coming from multiple sources. It contains five categories and covers a wide spectrum of images, ranging from true color images to the binary ones. The experimental results demonstrate that the proposed approach outperforms the solutions based on accumulative voting.