Coarse-to-Fine Search Technique to Detect Circles in Images

Detection of patterns in images is an important high-level task in automated manufacturing using machine vision. Straight lines, circles and ellipses are considered to be the basic building blocks of a large number of patterns occurring in real-world images. Real-world images frequently contain noise and occlusions resulting in discontinuous patterns in noisy images. The Hough transform can be used to detect parametric patterns, such as straight lines and circles, embedded in noisy images. The large amount of storage and computing power required by the Hough transform presents a problem in real-time applications.

The aim of this paper is to propose an efficient coarse-to-fine search technique to reduce the storage and computing time in detecting circles in an image. Variable-sized images and accumulator arrays are used to reduce the computing and storage requirements of the Hough transform. The accuracy and the rate of convergence of the parameters at different iterations of the algorithm are presented. The results demon-strate that the coarse-to-fine search strategy is very suitable for detecting circles in real-time environments having time constraints.