Binary image registration using covariant gaussian densities

Csaba Domokos, Zoltan Kato

Research output: Conference contribution

6 Citations (Scopus)

Abstract

We consider the estimation of 2D affine transformations aligning a known binary shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the two images and then compute the transformation parameters from these landmarks. In this paper, we propose a novel approach where the exact transformation is obtained as a least-squares solution of a linear system. The basic idea is to fit a Gaussian density to the shapes which preserves the effect of the unknown transformation. It can also be regarded as a consistent coloring of the shapes yielding two rich functions defined over the two shapes to be matched. The advantage of the proposed solution is that it is fast, easy to implement, works without established correspondences and provides a unique and exact solution regardless of the magnitude of transformation.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 5th International Conference, ICIAR 2008, Proceedings
Pages455-464
Number of pages10
DOIs
Publication statusPublished - júl. 28 2008
Event5th International Conference on Image Analysis and Recognition, ICIAR 2008 - Povoa de Varzim, Portugal
Duration: jún. 25 2008jún. 27 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5112 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Image Analysis and Recognition, ICIAR 2008
CountryPortugal
CityPovoa de Varzim
Period6/25/086/27/08

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Domokos, C., & Kato, Z. (2008). Binary image registration using covariant gaussian densities. In Image Analysis and Recognition - 5th International Conference, ICIAR 2008, Proceedings (pp. 455-464). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5112 LNCS). https://doi.org/10.1007/978-3-540-69812-8_45