Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/55716
Título: An iterative optimization algorithm for lens distortion correction using two-parameter models
Autores/as: Santana Cedres, Daniel Elias
Gómez Déniz, Luis
Alemán Flores, Miguel
Salgado de la Nuez, Agustín
Esclarín Monreal, Julio
Mazorra, L.
Alvarez, L
Clasificación UNESCO: 120602 Ecuaciones diferenciales
120326 Simulación
120601 Construcción de algoritmos
220990 Tratamiento digital. Imágenes
Palabras clave: Calibration
Straight
Fecha de publicación: 2016
Publicación seriada: Image Processing On Line 
Resumen: We present a method for the automatic estimation of two-parameter radial distortion models, considering polynomial as well as division models. The method first detects the longest distorted lines within the image by applying the Hough transform enriched with a radial distortion parameter. From these lines, the first distortion parameter is estimated, then we initialize the second distortion parameter to zero and the two-parameter model is embedded into an iterative nonlinear optimization process to improve the estimation. This optimization aims at reducing the distance from the edge points to the lines, adjusting two distortion parameters as well as the coordinates of the center of distortion. Furthermore, this allows detecting more points belonging to the distorted lines, so that the Hough transform is iteratively repeated to extract a better set of lines until no improvement is achieved. We present some experiments on real images with significant distortion to show the ability of the proposed approach to automatically correct this type of distortion as well as a comparison between the polynomial and division models.Source CodeThe source code, the code documentation, and the online demo are accessible at the IPOL web page of this articlel In this page, an implementation is available for download. Compilation and usage instructions are included in the README.txt file of the archive.
URI: http://hdl.handle.net/10553/55716
ISSN: 2105-1232
DOI: 10.5201/ipol.2016.130
Fuente: Image Processing On Line[ISSN 2105-1232],v. 6, p. 326-364, (2016)
Colección:Artículos
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