Paper Info
Title
Taut-String Algorithm and Regularization Programs with G-Norm Data Fit
Abstract
In this paper we derive a unified framework for the taut-string algorithm and regularization with G-norm data fit. The G-norm data fit criterion (popularized in image processing by Y. Meyer) has been paid considerable interest in regularization models for pattern recognition. The first numerical work based on G-norm data fit has been proposed by Osher and Vese. The taut-string algorithm has been developed in statistics (Mammen and van de Geer and Davies and Kovac) for denoising of one dimensional sample data of a discontinuous function. Recently Hinterberger et al. proposed an extension of the taut-string algorithm to higher dimensional data by introducing the concept of tube methods. Here we highlight common features between regularization programs with a G-norm data fit term and taut-string algorithms (respectively tube methods). This links the areas of statistics, regularization theory, and image processing.
Year
DOI
Venue
2005
10.1007/s10851-005-6462-1
Journal of Mathematical Imaging and Vision
Keywords
Field
DocType
regularization model,tube method,g-norm data fit,higher dimensional data,regularization program,regularization programs,regularization theory,taut-string,taut-string algorithm,g-norm,denoising,g-norm data,image processing,dimensional sample data,pattern recognition,data fitting
Noise reduction,Continuous function,Mathematical optimization,Image processing,Algorithm,Regularization (mathematics),Regularization theory,Mathematics,Regularization perspectives on support vector machines
Journal
Volume
Issue
ISSN
23
2
0924-9907
Citations 
PageRank 
References 
4
0.74
4
Authors
1
Name
Order
Citations
PageRank
Otmar Scherzer134652.10