Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/53414
Title: Comparing self-calibration methods for static cameras
Authors: Gonzalez, J. Isern 
Cabrera Gámez, J. 
Hernández-Sosa, J. D. 
Domínguez-Brito, A. C. 
UNESCO Clasification: 120304 Inteligencia artificial
Issue Date: 2007
Publisher: 0302-9743
Project: Aprendizaje Interactivo de Mapas Multisensoriales en Robótica Móvil. 
Orientación de Robots Móviles Mediante Detección Visual de Marcas Naturales. 
Journal: Lecture Notes in Computer Science 
Conference: 11th International Conference on Computer Aided Systems Theory 
Abstract: Many methods have been developed in the last few years to self-calibrate cameras, but few works have addressed the comparison of such methods to provide the user with hints on the suitability of certain algorithms under particular circumstances. This work presents a comparative analysis of four self-calibration methods for cameras which only rotate. This paper concentrates on the stability, the accuracy in the estimation of each parameter and the computational cost. This study has been carried out with real and simulated images. The experiments have shown that the optic center is the most unstable parameter for all methods and that the greatest discrepancies among the estimated values appear with the scale factors. Also, there are no correspondence among image disparity and parameters error. Finally, the results returned by any of these methods are comparable in terms of accuracy with those provided by a well-known manual calibration method.
URI: http://hdl.handle.net/10553/53414
ISBN: 978-3-540-75866-2
ISSN: 0302-9743
DOI: 10.1007/978-3-540-75867-9_83
Source: Moreno Díaz R., Pichler F., Quesada Arencibia A. (eds) Computer Aided Systems Theory – EUROCAST 2007. Lecture Notes in Computer Science, vol 4739, p. 660-667. Springer, Berlin, Heidelberg
Appears in Collections:Actas de congresos
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