Homography estimation using local affine frames

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

1 Scopus citations

Abstract

The homography between pairs of images is typically computed from the correspondence of key features such as points, lines, conics and other geometric entities, each contributing information to fix the required 8 degrees of freedom (DOF). In the past years there have been attempts to use conics as correspondence features as a minimum of two correspondence pairs are required. However, the resulting methods either place restrictions on the problem (such as pure camera rotation, known calibration) or result in iterative non-linear solutions or in over-parameterized linear problems. Throughout this paper we propose a simple, direct linear transformation (DLT) like solution to the problem of homography estimation using local affine frames, without any restrictions on the physical model. We also provide approximated statistical analysis of the proposed algorithm and a comparison with the performance of the DLT algorithm. In addition, this method can be easily adapted to similar problems which employ a DLT-like algorithm.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages1981-1985
Number of pages5
DOIs
StatePublished - 18 Oct 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 26 May 201331 May 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period26/05/1331/05/13

Keywords

  • Homography estimation
  • Local affine frames
  • Perturbation analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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