Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/45745
Título: | Automatic corner matching in highly distorted images of Zhang’s calibration pattern | Autores/as: | Alemán-Flores, Miguel Alvarez, Luis Gomez, Luis Santana-Cedrés, Daniel |
Clasificación UNESCO: | 220990 Tratamiento digital. Imágenes 120601 Construcción de algoritmos 120602 Ecuaciones diferenciales 120326 Simulación |
Palabras clave: | Camera calibration Lens distortion Zhang’s method |
Fecha de publicación: | 2014 | Editor/a: | Springer | Proyectos: | Modelización Matemática de Los Procesos de Calibración de Cámaras de Video. | Publicación seriada: | Lecture Notes in Computer Science | Conferencia: | 19th Iberoamerican Congress on Pattern Recognition (CIARP 2014) | Resumen: | Zhang’s method is a widely used technique for camera calibration from different views of a planar calibration pattern. This pattern contains a set of squares arranged in a certain configuration. In order to calibrate the camera, the corners of the squares in the images must be matched with those in the reference model. When the images show a strong lens distortion, the usual methods to compute the corner matching fail because the corners are shifted from their expected positions. We propose a new method which automatically estimates such corner matching taking into account the lens distortion. The method is based on an automatic algorithm for lens distortion correction which allows estimating the distorted lines passing through the edges of the squares. We present some experiments to illustrate the performance of the proposed method, as well as a comparison with the usual technique proposed in a Matlab toolbox. | URI: | http://hdl.handle.net/10553/45745 | ISBN: | 978-3-319-12567-1 | ISSN: | 0302-9743 | DOI: | 10.1007/978-3-319-12568-8_91 | Fuente: | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, v. 8827 LNCS, p. 754-761 |
Colección: | Capítulo de libro |
Citas SCOPUSTM
1
actualizado el 17-nov-2024
Citas de WEB OF SCIENCETM
Citations
1
actualizado el 30-may-2021
Visitas
168
actualizado el 28-sep-2024
Descargas
39
actualizado el 28-sep-2024
Google ScholarTM
Verifica
Altmetric
Comparte
Exporta metadatos
Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.