Sampson distance based joint estimation of multiple homographies with uncalibrated cameras

•Practitioners typically estimate multiple homographies separately.•Separate estimation does not enforce inter-homography constraints.•We jointly estimate homographies so that all constraints are satisfied.•Enforcing inter-homography constraints improves accuracy considerably.•Our cost function oper...

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Veröffentlicht in:	Computer vision and image understanding Jg. 125; S. 200 - 213
Hauptverfasser:	Szpak, Zygmunt L., Chojnacki, Wojciech, Eriksson, Anders, van den Hengel, Anton
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Inc 01.08.2014 Elsevier
Schlagworte:	Applied sciences Artificial intelligence Cameras Collection Computer science; control theory; systems Computer vision Cost function Data points Estimates Exact sciences and technology Fittings Latent variables Mathematics Maximum likelihood Multiple homographies Parameter estimation Parametric inference Pattern recognition. Digital image processing. Computational geometry Probability and statistics Sampson distance Sciences and techniques of general use Statistics Sampson distance Multiple homographies Latent variables Parameter estimation Maximum likelihood Image processing Epipolar geometry Image databank Latent variable model Modeling Multiple decision Multiple view Latent variable Image analysis Scene analysis Homograph Hidden variable theory Problem solving Cost function System identification Multiple choice
ISSN:	1077-3142, 1090-235X
Online-Zugang:	Volltext
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