Multimodal Signal, Image and Video Analysis and Application
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 11308
Special Issue Editors
Interests: computer vision; pattern recognition; machine learning
Interests: computer vision and machine learning, especially data/label- and computation-efficient deep learning for visual recognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the increasing requirement of fine-scale representation of the physical world, various data capturing techniques have been developed, which leads to a wide range of modalities for signals, images and videos. It is of particular importance to study the use of multimodal information to understand events in real-world application scenarios, e.g., object detection using RGBD and LiDAR data, surface defect inspection using optical and radar data, mural painting analysis using X-ray and multi-spectrum data, information retrieval using images and texts, etc. This Special Issue aims at promoting cutting-edge research in multimodal information processing and offers a timely collection of works to benefit researchers in both academia and industry. We welcome high-quality original submissions addressing either theoretical or practical issues. Topics of interests include, but are not limited to:
- Multimodal data acquisition techniques;
- Multimodal data storage and indexing techniques;
- Modeling, representation, and learning with multimodal data;
- Multimodal data fusion;
- Cross-modal data mining and retrieval;
- Visualization and interpretation techniques for multimodal data;
- Machine learning with multimodal data;
- Open issues for multimodal data analysis and application.
Dr. Qin Zou
Prof. Dr. Xinggang Wang
Dr. Long Chen
Guest Editors
Manuscript Submission Information
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Keywords
- multimodal data
- image processing
- video processing
- machine learning
- information retrieval
- data fusion