An Incremental Hough Transform for Detecting Ellipses in Image Data Streams

In this paper, we present a purely incremental, scalable algorithm for the detection of elliptical shapes in images. Our method uses an incremental version of the Random Hough Transform (RHT) to compute the curve parameters from sampled image points and uses a density-based robust stream clustering algorithm to discover the potential parameters from the Hough space. Finally we apply density and similarity tests to eliminate weak and redundant candidates. Being totally incremental, and not requiring the typically huge memory costs of Hough accumulator arrays or image pixels, our method reduces the number of computations performed and the memory used. The proposed method is tested on both synthetic and real images, including solar images captured by various instruments onboard NASA and ESA satellites.