Randomized Hough Transform for Ellipse Detection with Result Clustering

Abstract

Our research is focused on the development of robust machine vision algorithms for pattern recognition. We want to provide robotic systems the ability to understand more on the external real world. In this paper we describe a method for detecting ellipses in real world images using the Randomized Hough Transform with Result Clustering. A preprocessing phase is used in which real world images are transformed - noise reduction, greyscale transform, edge detection and finaly binarization - in order to be processed by the actual ellipse detector. The ellipse detector filters out false ellipses that may interfere with the final results. Due to the fact that usually more "virtual" ellipses are detected for one "real" ellipse, a data clustering scheme is used, the clustering method, classifies all detected "virtual" ellipses into their corresponding "real" ellipses. The post processing phase is VQ similar and it also finds the actual number of classes unknown a priori.