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Hybridnet for depth estimation and semantic segmentation

Autor
Sánchez, D.; Lin, X.; Casas, J.; Pardas, M.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing
Any de l'edició
2018
Data de presentació
2018-06-13
Llibre d'actes
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing: proceedings: April 15-20, 2018 Calgary: Telus Convention Center: Calgary: Alberta, Canada
Pàgina inicial
1563
Pàgina final
1567
Editor
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/ICASSP.2018.8462433 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/122785 Obrir en finestra nova
URL
https://ieeexplore.ieee.org/document/8462433 Obrir en finestra nova
Resum
Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric and semantic information is required, such as robotics or autonomous navigation, depth or semantic segmentation alone are not sufficient. In this paper, depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional netwo...
Citació
Sánchez, D., Lin, X., Casas, J., Pardas, M. Hybridnet for depth estimation and semantic segmentation. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1563-1567.
Paraules clau
Depth estimation, Hybrid convolutional network, Semantic segmentation
Grup de recerca
GPI - Grup de Processament d'Imatge i Vídeo
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants