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Hybridnet for depth estimation and semantic segmentation

Author
Sánchez, D.; Lin, X.; Casas, J.; Pardas, M.
Type of activity
Presentation of work at congresses
Name of edition
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing
Date of publication
2018
Presentation's date
2018-06-13
Book of congress proceedings
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing: proceedings: April 15-20, 2018 Calgary: Telus Convention Center: Calgary: Alberta, Canada
First page
1563
Last page
1567
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/ICASSP.2018.8462433 Open in new window
Repository
http://hdl.handle.net/2117/122785 Open in new window
URL
https://ieeexplore.ieee.org/document/8462433 Open in new window
Abstract
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...
Citation
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.
Keywords
Depth estimation, Hybrid convolutional network, Semantic segmentation
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants