General technique for smoothing multi-dimensional datasets utilizing orthogonal expansions and lower dimensional smoothers

Mark A. Anastasio, Xiaochuan Pan, Chien Min Kao

Research output: Contribution to conferencePaperpeer-review

Abstract

Smoothing methods are often used for discerning underlying patterns and structure concealed by statistical noise within a dataset. Adaptive smoothing approaches are particularly useful because the amount of smoothing imposed on the data is determined automatically from the statistical characteristics of subsets of the data itself. In this work, we show theoretically that an effective N-dimensional smoothing can be achieved by utilization of a series of non-identical n-dimensional (n < N) smoothings on the coefficients of a partial orthogonal expansion of the data function. This result provides a framework in which one-dimensional adaptive smoothing methods may be applied to higher-dimensional data. We applied this generalized multi-dimensional data. We applied this generalized image and demonstrate that one can obtain a two-dimensional smoothing effect by applying a series of one-dimensional smoothings to the coefficients of the partial orthogonal expansion of the image.

Original languageEnglish (US)
Pages718-721
Number of pages4
StatePublished - Dec 1 1998
Externally publishedYes
EventProceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) - Chicago, IL, USA
Duration: Oct 4 1998Oct 7 1998

Other

OtherProceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3)
CityChicago, IL, USA
Period10/4/9810/7/98

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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