Robust regression of scattered data with adaptive spline-wavelets

Daniel Castaño; Angela Kunoth

doi:10.1109/tip.2006.871164

Robust regression of scattered data with adaptive spline-wavelets

IEEE Trans Image Process. 2006 Jun;15(6):1621-32. doi: 10.1109/tip.2006.871164.

Authors

Daniel Castaño¹, Angela Kunoth

Affiliation

¹ European Molecular Biology Laboratory, Heidelberg, Germany. castano@embl.de

PMID: 16764286
DOI: 10.1109/tip.2006.871164

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 Castaño and Kunoth, 2003. This method has been extended in Castaño 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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Artificial Intelligence*
Computer Simulation
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods*
Information Storage and Retrieval / methods
Models, Statistical
Regression Analysis
Sample Size
Signal Processing, Computer-Assisted