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3D point cloud segmentation using a fully connected conditional random field

Autor
Lin, X.; Casas, J.; Pardas, M.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
25th European Signal Processing Conference
Any de l'edició
2017
Data de presentació
2017-08-29
Llibre d'actes
2017 25th European Signal Processing Conference: EUSIPCO 2017: Kos, Greece: 28 August-2 September 2017
Pàgina inicial
66
Pàgina final
70
Editor
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.23919/EUSIPCO.2017.8081170 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/119742 Obrir en finestra nova
URL
https://ieeexplore.ieee.org/document/8081170/ Obrir en finestra nova
Resum
Traditional image segmentation methods working with low level image features are usually difficult to adapt to higher level tasks, such as object recognition and scene understanding. Object segmentation emerges as a new challenge in this research field. It aims at obtaining more meaningful segments related to semantic objects in the scene by analyzing a combination of different information. 3D point cloud data obtained from consumer depth sensors has been exploited to tackle many computer vision...
Citació
Lin, X., Casas, J., Pardas, M. 3D point cloud segmentation using a fully connected conditional random field. A: European Signal Processing Conference. "2017 25th European Signal Processing Conference: EUSIPCO 2017: Kos, Greece: 28 August-2 September 2017". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 66-70.
Paraules clau
Computer vision, Image segmentation, Image sequences, Minimisation, Object recognition
Grup de recerca
GPI - Grup de Processament d'Imatge i Vídeo
IDEAI-UPC Intelligent Data Science and Artificial Intelligence

Participants