Título:
Finding vanishing points via point alignments in image primal and dual domains
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Autor:
Morel, Jean-Michel
Randall, Gregory
Grompone von Gioi, Rafael
Lezama, José
Randall, Gregory
Grompone von Gioi, Rafael
Lezama, José
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
Vanishing point detection
Line-to-point mapping
2d point alignments
Line-to-point mapping
2d point alignments
Descriptores:
Procesamiento de Señales
Año de publicación:
2014
Contenido:
Resumen:
We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection. The very same point alignment detection algorithm is used twice: First in the image domain to group line segment endpoints into more precise lines. Second, it is used in the dual domain where converging lines become aligned points. The use of the recently introduced PClines dual spaces and a robust point alignment detector leads to a very accurate algorithm. Experimental results on two public standard datasets show that our method significantly advances the state-of-the-art in the Manhattan world scenario, while producing state-of-the-art performances in non-Manhattan scenes.
Descripción:
Trabajo publicado en IEEE Conference on Computer Vision and Pattern Recognition, la versión open access que se publica es suministrada por Computer Vision Foundation. La versión autorizada puede consultarse en IEEE Xplore. DOI: 10.1109/CVPR.2014.72
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Editorial:
Computer Vision Foundation
EN:
Conference pone von Gioi, R, Randall, G, Morel, J.M."Finding Vanishing Points via Point Alignments in Image Primal and Dual Domains," Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23-28 jun., 2014
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Citación:
Lezama, J, Grompone von Gioi, R, Randall, G, Morel, J.M."Finding Vanishing Points via Point Alignments in Image Primal and Dual Domains," Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23-28 jun., 2014.
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Tratamiento de Imágenes
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| LGMR14a.pdf | — | 6.15 MB | Adobe PDF |