Multi-Modality Sensing Data Analysis and Its Application in Image Processing and Vision
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 604
Special Issue Editors
Interests: computer vision; image processing; machine/deep learning; scientific computing
Special Issues, Collections and Topics in MDPI journals
Interests: image processing; optimization; tensor analysis; computing
Interests: scientific computing; machine learning; computer vision; artificial intelligence; sparse and low rank modeling for high dimensional data analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in sensing technologies have enabled the acquisition of rich multi-modality data, including images, videos, depth, radar, LiDAR, hyperspectral data, biomedical data, and other heterogeneous sensor signals. Effectively analyzing and fusing such multi-modality sensing data has become a critical research topic, particularly for image processing and computer vision applications, where complementary information from different sensors can significantly enhance perception, robustness, and interpretability.
This Special Issue will provide a comprehensive forum for researchers and practitioners to present the latest theoretical developments, algorithmic innovations, and practical applications related to multi-modality sensing data analysis and its integration into image processing and vision systems. Topics of interest include, but are not limited to, multi-sensor data fusion, cross-modality representation learning, deep learning for multi-modal perception, and real-world applications such as autonomous systems, remote sensing, medical imaging, smart cities, and human–computer interaction.
By bringing together cutting-edge methodologies and emerging applications, this Special Issue will advance the state of the art and foster interdisciplinary collaboration in multi-modality sensing and vision research.
Dr. Xiaohao Cai
Prof. Dr. Gaohang Yu
Prof. Dr. Xi-Le Zhao
Guest Editors
Dr. Jiahui Liu
Guest Editor Assistant
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Keywords
- multi-sensor data fusion
- multi-modality sensing
- multimodal data analysis
- multimodal representation learning
- cross-modality learning
- feature extraction and fusion
- trustworthy and interpretable multimodal AI
- image processing
- computer vision
- deep learning for multimodal data
- vision-based sensing
- three-dimensional vision and depth sensing
- hyperspectral and multispectral imaging
- LiDAR, radar, and RGB data integration
- medical image analysis
- remote sensing and earth observation
- autonomous systems and robotics
- human–computer interaction
- intelligent perception systems
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