Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/53414
Título: Comparing self-calibration methods for static cameras
Autores/as: Gonzalez, J. Isern 
Cabrera Gámez, J. 
Hernández-Sosa, J. D. 
Domínguez-Brito, A. C. 
Clasificación UNESCO: 120304 Inteligencia artificial
Fecha de publicación: 2007
Editor/a: 0302-9743
Proyectos: Aprendizaje Interactivo de Mapas Multisensoriales en Robótica Móvil. 
Orientación de Robots Móviles Mediante Detección Visual de Marcas Naturales. 
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 11th International Conference on Computer Aided Systems Theory 
Resumen: 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
Fuente: 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
Colección:Actas de congresos
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