Robust estimation of shape parameters

We investigate the use of Robust Estimation in an application requiring the accurate location of the centres of circular objects in an image. A common approach used throughout computer vision for extracting shape information from a data set is to fit a feature model using the Least Squares method. The well known sensitivity of this method to outliers is traditionally accommodated by outlier rejection methods. These usually consist of heuristic applications of model templates or data trimming. Robust Estimation offers a theoretical framework for assessing such rejection schemes, and more importantly, provides an approach to parameter estimation in contaminated data distributions capable of greater accuracy.