Event-Based Vision and Multimodal Sensor Fusion
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 20 August 2026 | Viewed by 66
Special Issue Editors
Interests: image enhancement; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: multispectral imaging processing; object detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Event-based vision has recently emerged as a disruptive sensing paradigm, inspired by the neuromorphic principles of the human retina. Unlike conventional frame-based cameras, event-based sensors asynchronously capture intensity changes with microsecond precision and a wide dynamic range, making them well-suited for fast motion, low-light, and high dynamic range environments. In parallel, multimodal sensor fusion has become an essential strategy to overcome the limitations of single modalities, enabling robust and comprehensive perception by integrating complementary information from diverse sensors, such as RGB cameras, LiDAR, radar, infrared, and inertial units.
This Special Issue aims to provide a platform for the latest developments in event-based vision and multimodal sensor fusion, covering both theoretical foundations and practical applications. It will highlight cutting-edge research on algorithms, architectures, and systems that advance the state-of-the-art in intelligent sensing and perception.
We welcome contributions that span a broad range of topics, including, but not limited to, event-based imaging and reconstruction, fusion frameworks, learning paradigms for cross-modal perception, and real-world applications in autonomous driving, robotics, augmented reality, biomedical imaging, and remote sensing. Both original research articles and comprehensive reviews are encouraged.
Dr. Yi Chang
Prof. Dr. Luxin Yan
Guest Editors
Dr. Haoyue Liu
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- event-based vision
- multimodal sensor fusion
- cross-modal learning
- autonomous systems and robotics
- intelligent perception systems
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