Efficient Recovery of Essential Matrix From Two Affine Correspondences

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00324018" target="_blank" >RIV/68407700:21230/18:00324018 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8392782" target="_blank" >https://ieeexplore.ieee.org/document/8392782</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIP.2018.2849866" target="_blank" >10.1109/TIP.2018.2849866</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient Recovery of Essential Matrix From Two Affine Correspondences

Popis výsledku v původním jazyce

We propose a method to estimate the essential matrix using two affine correspondences for a pair of calibrated perspective cameras. Two novel, linear constraints are derived between the essential matrix and a local affine transformation. The proposed method is also applicable to the over-determined case. We extend the normalization technique of Hartley to local affinities and show how the intrinsic camera matrices modify them. Even though perspective cameras are assumed, the constraints can straightforwardly be generalized to arbitrary camera models since they describe the relationship between local affinities and epipolar lines (or curves). Benefiting from the low number of exploited points, it can be used in robust estimators, e.g. RANSAC, as an engine, thus leading to significantly less iterations than the traditional point-based methods. The algorithm is validated both on synthetic and publicly available data sets and compared with the state-of-the-art. Its applicability is demonstrated on two-view multi-motion fitting, i.e., finding multiple fundamental matrices simultaneously, and outlier rejection.

Název v anglickém jazyce

Efficient Recovery of Essential Matrix From Two Affine Correspondences

Popis výsledku anglicky

We propose a method to estimate the essential matrix using two affine correspondences for a pair of calibrated perspective cameras. Two novel, linear constraints are derived between the essential matrix and a local affine transformation. The proposed method is also applicable to the over-determined case. We extend the normalization technique of Hartley to local affinities and show how the intrinsic camera matrices modify them. Even though perspective cameras are assumed, the constraints can straightforwardly be generalized to arbitrary camera models since they describe the relationship between local affinities and epipolar lines (or curves). Benefiting from the low number of exploited points, it can be used in robust estimators, e.g. RANSAC, as an engine, thus leading to significantly less iterations than the traditional point-based methods. The algorithm is validated both on synthetic and publicly available data sets and compared with the state-of-the-art. Its applicability is demonstrated on two-view multi-motion fitting, i.e., finding multiple fundamental matrices simultaneously, and outlier rejection.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Image Processing

ISSN

1057-7149

e-ISSN

1941-0042

Svazek periodika

27

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

5328-5337

Kód UT WoS článku

000440203500010

EID výsledku v databázi Scopus

2-s2.0-85049090983