YUAN, XIN;
BRADY, DAVIDJ;
SUO, JINLI;
ARGUELLO, HENRY;
RODRIGUES, MIGUEL;
KATSAGGELOS, AGGELOSK;
(2022)
Editorial: Introduction to the Special Issue on Deep Learning for High-Dimensional Sensing.
IEEE Journal of Selected Topics in Signal Processing
, 16
(4)
pp. 603-607.
10.1109/JSTSP.2022.3185190.
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Abstract
The papers in this special section focus on deep learning for high-dimensional sensing. People live in a high-dimensional world and sensing is the first step to perceive and understand the environment for both human beings and machines. Therefore, high-dimensional sensing (HDS) plays a pivotal role in many fields such as robotics, signal processing, computer vision and surveillance. The recent explosive growth of artificial intelligence has provided new opportunities and tools for HDS, especially for machine vision. In many emerging real applications such as advanced driver assistance systems/autonomous driving systems, large-scale, high-dimensional and diverse types of data need to be captured and processed with high accuracy and in a real-time manner. Bearing this in mind, now is the time to develop new sensing and processing techniques with high performance to capture high-dimensional data by leveraging recent advances in deep learning (DL).
| Type: | Article |
|---|---|
| Title: | Editorial: Introduction to the Special Issue on Deep Learning for High-Dimensional Sensing |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JSTSP.2022.3185190 |
| Publisher version: | https://doi.org/10.1109/JSTSP.2022.3185190 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Engineering, Electrical & Electronic, Engineering, Special issues and sections, Deep learning, Robot sensing systems, Surveillance, Signal processing, Machine vision, Computer vision, Artificial intelligence, Sensors |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10172100 |
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