Modeling 2D image data by robust M-estimation

The conventional least squared distance method of fitting a model to a set of data points gives unreliable results when the amount of noise in the input is significant compared with the amount of data correlated to the model itself. The theory of robust statistics formally addresses these problems and is used in this work to develop a method of separation of the data of interest from noise. It is based on iteratively reweighted least squares algorithm where Hampel redescending function is applied for weighting data. The method has been efficiently tested in modeling synthetic and real 2D image data with second order curves.