Analytic outlier removal in line fitting

Author

Netanyahu, Nathan S. ; Weiss, Isaac

Author_Institution

Div. of Space Data & Comput., NASA Goddard Space Flight Center, Greenbelt, MD, USA

Volume

2

fYear

1994

fDate

9-13 Oct 1994

Firstpage

406

Abstract

The conventional ordinary least squares (OLS) method of fitting a line to a set of data points is very unreliable when the amount of random noise in the input (such as an image) is significant compared with the amount of data that is correlated with the lane itself. In this paper we present an analytic method of separating the data of interest from the outliers. We assume that the overall data (i.e., the line data plus the noise) can be modeled as a mixture of two statistical distributions. Applying a variant of the method of moments (MoM) to the assumed model yields an analytic estimate of the desired line

Keywords

curve fitting; analytic outlier removal; data separation; line fitting; method of moments; mixture models; noise removal; statistical distributions; Automation; Educational institutions; Image processing; Least squares methods; Message-oriented middleware; Moment methods; NASA; Noise generators; Parameter estimation; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on

Conference_Location

Jerusalem

Print_ISBN

0-8186-6270-0

Type

conf

DOI

10.1109/ICPR.1994.576962

Filename

576962