Beyond Monocular Computer Vision: Other Sensors, Multimedia and Multi-View Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 May 2025 | Viewed by 780
Special Issue Editors
Interests: computer vision; machine learning; sensors; HRI
Interests: computer vision; machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Computer vision was born and developed with monocular cameras, which laid the foundation for many early advancements in the field. However, the advent of innovative sensors such as depth cameras, event-based cameras, thermal cameras, and LiDAR and the integration of multimedia and multi-camera systems are now addressing the inherent limitations of monocular vision by providing richer and more dynamic data.
This Special Issue welcomes contributions that explore recent advancements with innovative sensors like event cameras or depth sensors, as well as multi-camera systems, pushing the boundaries of what is possible with traditional computer vision.
Submissions to this Special Issue on ‘Beyond monocular computer vision: other sensors, multimedia, and multi-view systems’ are encouraged to provide recent advances by covering a range of topics such as (but not limited to) the following computer vision theory and applications:
- Depth sensors;
- Event-cameras and event-based sensors;
- Multi-camera systems and multi-view geometry;
- Multimedia systems;
- Machine learning applied to novel sensors;
- Light Detection and Ranging (LiDAR);
- TOF cameras;
- Thermal cameras;
- Ultrasound sensors;
- Multispectral/hyperspectral cameras;
- Scanning electron microscope (SEM);
- Omnidirectional cameras.
Dr. Dario Cazzato
Dr. Marco Leo
Guest Editors
Manuscript Submission Information
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Keywords
- sensors
- computer vision
- machine learning
- event-cameras
- depth sensors
- multi-camera systems
- multimedia
- multispectral/hyperspectral imaging
- LiDAR
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