Title :
Robust regression of scattered data with adaptive spline-wavelets
Author :
Castaño, Daniel ; Kunoth, Angela
Author_Institution :
Inst. for Numerical Simulation, Univ. of Bonn, Germany
fDate :
6/1/2006 12:00:00 AM
Abstract :
A coarse-to-fine data fitting algorithm for irregularly spaced data based on boundary-adapted adaptive tensor-product semi-orthogonal spline-wavelets has been proposed in Castano and Kunoth, 2003. This method has been extended in Castano and Kunoth, 2005 to include regularization in terms of Sobolev and Besov norms. In this paper, we develop within this least-squares approach some statistical robust estimators to handle outliers in the data. Our wavelet scheme yields a numerically fast and reliable way to detect outliers.
Keywords :
adaptive estimation; image processing; least squares approximations; regression analysis; splines (mathematics); wavelet transforms; adaptive spline-wavelets; coarse-to-fine data fitting algorithm; least-squares approach; scattered data regression; semiorthogonal spline-wavelets; Clouds; Computational efficiency; Government; Laboratories; Least squares approximation; Mathematical analysis; Robustness; Scattering; Spline; Tensile stress; Adaptive wavelets; coarse-to-fine algorithm; outlier detection; robust estimation; scattered data; Algorithms; Artificial Intelligence; Computer Simulation; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Models, Statistical; Regression Analysis; Sample Size; Signal Processing, Computer-Assisted;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2006.871164
