Decoupled sequential estimation of homeomorphic deformations

Rami Hagege, Joseph M. Francos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider the problem of object and image registration, where the observation is assumed to be the result of a geometric deformation of a known template. More specifically, in the current framework we are interested in the case where the overall deformation is a result of a sequence of deformations the object has undergone and one is interested in estimating only part of the sequence, in a way that is invariant to the existence of the other deformations. The simplest example is the case where the deformation is af̌ne, but one is interested only in estimating the scaling and rotation, irrespectively of the possible translation. In this paper we present a general framework allowin gone to " focus" only on the parts of interest in the deformation, being invariant to "nuisance" parts of the overall deformation. This is achieved by transforming the original problem in which the deformation parameters appear implicitly rather than explicitly, into an equivalent problem which is explicit and linear in the deformation parameters. The representation of the original problem in the "classic" framework of linear spaces enables the adoption of well known linear methods.

Original languageEnglish
Title of host publicationProceedings of the 9th IASTED International Conference on Signal and Image Processing, SIP 2007
Pages517-521
Number of pages5
StatePublished - 1 Dec 2007
Event9th IASTED International Conference on Signal and Image Processing, SIP 2007 - Honolulu, HI, United States
Duration: 20 Aug 200722 Aug 2007

Publication series

NameProceedings of the 9th IASTED International Conference on Signal and Image Processing, SIP 2007

Conference

Conference9th IASTED International Conference on Signal and Image Processing, SIP 2007
Country/TerritoryUnited States
CityHonolulu, HI
Period20/08/0722/08/07

Keywords

  • Image recognition
  • Image registration
  • Multidimensional signal processing
  • Nonlinear estimation
  • Parameter estimation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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