Single axis geometry by fitting conics

<p>In this paper, we describe a new approach for recovering 3D geometry from an uncalibrated image sequence of a single axis (turn-table) motion. Unlike previous methods, the computation of multiple views encoded by the fundamental matrix or trifocal tensor is not required. Instead, the new ap...

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Main Authors: Jiang, G, Tsui, H-T, Quan, L, Zisserman, A
Format: Conference item
Language:English
Published: Springer 2002
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author Jiang, G
Tsui, H-T
Quan, L
Zisserman, A
author_facet Jiang, G
Tsui, H-T
Quan, L
Zisserman, A
author_sort Jiang, G
collection OXFORD
description <p>In this paper, we describe a new approach for recovering 3D geometry from an uncalibrated image sequence of a single axis (turn-table) motion. Unlike previous methods, the computation of multiple views encoded by the fundamental matrix or trifocal tensor is not required. Instead, the new approach is based on fitting a conic locus to corresponding image points over multiple views. It is then shown that the geometry of single axis motion can be recovered given at least two such conics. In the case of two conics the reconstruction may have a two fold ambiguity, but this ambiguity is removed if three conics are used.</p> <p>The approach enables the geometry of the single axis motion (the 3D rotation axis and Euclidean geometry in planes perpendicular to this axis) to be estimated using the minimal number of parameters. It is demonstrated that a Maximum Likelihood Estimation results in measurements that are as good as or superior to those obtained by previous methods, and with a far simpler algorithm. Examples are given on various real sequences, which show the accuracy and robustness of the new algorithm.</p>
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spelling oxford-uuid:aadad54e-0e6c-4453-9c1b-b8a9aaa07ca32025-01-28T13:37:38ZSingle axis geometry by fitting conicsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:aadad54e-0e6c-4453-9c1b-b8a9aaa07ca3EnglishSymplectic ElementsSpringer2002Jiang, GTsui, H-TQuan, LZisserman, A<p>In this paper, we describe a new approach for recovering 3D geometry from an uncalibrated image sequence of a single axis (turn-table) motion. Unlike previous methods, the computation of multiple views encoded by the fundamental matrix or trifocal tensor is not required. Instead, the new approach is based on fitting a conic locus to corresponding image points over multiple views. It is then shown that the geometry of single axis motion can be recovered given at least two such conics. In the case of two conics the reconstruction may have a two fold ambiguity, but this ambiguity is removed if three conics are used.</p> <p>The approach enables the geometry of the single axis motion (the 3D rotation axis and Euclidean geometry in planes perpendicular to this axis) to be estimated using the minimal number of parameters. It is demonstrated that a Maximum Likelihood Estimation results in measurements that are as good as or superior to those obtained by previous methods, and with a far simpler algorithm. Examples are given on various real sequences, which show the accuracy and robustness of the new algorithm.</p>
spellingShingle Jiang, G
Tsui, H-T
Quan, L
Zisserman, A
Single axis geometry by fitting conics
title Single axis geometry by fitting conics
title_full Single axis geometry by fitting conics
title_fullStr Single axis geometry by fitting conics
title_full_unstemmed Single axis geometry by fitting conics
title_short Single axis geometry by fitting conics
title_sort single axis geometry by fitting conics
work_keys_str_mv AT jiangg singleaxisgeometrybyfittingconics
AT tsuiht singleaxisgeometrybyfittingconics
AT quanl singleaxisgeometrybyfittingconics
AT zissermana singleaxisgeometrybyfittingconics