Radial Distortion Homography

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00321165" target="_blank" >RIV/68407700:21230/15:00321165 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/15:00235489 RIV/68407700:21730/15:00321165

Výsledek na webu

<a href="https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Kukelova_Radial_Distortion_Homography_2015_CVPR_paper.pdf" target="_blank" >https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Kukelova_Radial_Distortion_Homography_2015_CVPR_paper.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR.2015.7298663" target="_blank" >10.1109/CVPR.2015.7298663</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Radial Distortion Homography

Popis výsledku v původním jazyce

The importance of precise homography estimation is often underestimated even though it plays a crucial role in various vision applications such as plane or planarity detection, scene degeneracy tests, camera motion classification, image stitching, and many more. Ignoring the radial distortion component in homography estimation-even for classical perspective cameras-may lead to significant errors or totally wrong estimates. In this paper, we fill the gap among the homography estimation methods by presenting two algorithms for estimating homography between two cameras with different radial distortions. Both algorithms can handle planar scenes as well as scenes where the relative motion between the cameras is a pure rotation. The first algorithm uses the minimal number of five image point correspondences and solves a nonlinear system of polynomial equations using Grobner basis method. The second algorithm uses a non-minimal number of six image point correspondences and leads to a simple system of two quadratic equations in two unknowns and one system of six linear equations. The proposed algorithms are fast, stable, and can be efficiently used inside a RANSAC loop.

Název v anglickém jazyce

Radial Distortion Homography

Popis výsledku anglicky

The importance of precise homography estimation is often underestimated even though it plays a crucial role in various vision applications such as plane or planarity detection, scene degeneracy tests, camera motion classification, image stitching, and many more. Ignoring the radial distortion component in homography estimation-even for classical perspective cameras-may lead to significant errors or totally wrong estimates. In this paper, we fill the gap among the homography estimation methods by presenting two algorithms for estimating homography between two cameras with different radial distortions. Both algorithms can handle planar scenes as well as scenes where the relative motion between the cameras is a pure rotation. The first algorithm uses the minimal number of five image point correspondences and solves a nonlinear system of polynomial equations using Grobner basis method. The second algorithm uses a non-minimal number of six image point correspondences and leads to a simple system of two quadratic equations in two unknowns and one system of six linear equations. The proposed algorithms are fast, stable, and can be efficiently used inside a RANSAC loop.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

CVPR 2015: Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

ISBN

978-1-4673-6964-0

ISSN

1063-6919

e-ISSN

—

Počet stran výsledku

9

Strana od-do

639-647

Název nakladatele

IEEE Computer Society Press

Místo vydání

New York

Místo konání akce

Boston

Datum konání akce

7. 6. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000387959200070