Structural Networks for Image Application
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 578
Special Issue Editors
Interests: view/image separation; image generation
Interests: object detection; computer vision; medical image analysis and processing; pattern recognition
2. Guangdong Province Key Laboratory of Information Security Technology, Guangzhou, China
3. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Beijing, China
Interests: computer vision; 3D human video prediction and generation; multimodal video understanding; multimodal large models; ocean large models; trajectory prediction
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence security; autonomous driving security; IoT security; computational visual security; software vulnerability analysis; formal methods; embedded systems; edge AI; microservices
Special Issue Information
Dear Colleagues,
Structural networks are a transformative approach to image application, integrating principles from graph theory, topology, computational geometry, deep learning, and more. They focus on modeling complex relationships within image data. This paradigm enables the capture of hierarchical, spatial, and semantic dependencies critical for advanced image understanding. As an important component of neural networks, structural networks enable models to parse intricate visual scenes, infer contextual relationships, and generate robust representations that transcend pixel-level analysis.
The scope of this Special Issue encompasses advances in structural network methodologies and their innovative applications in imaging domains, including, but not limited to, computer vision, pattern recognition, natural language understanding, intelligent robotics, and deep learning.
This Special Issue aims to build a collaborative community for researchers to present cutting-edge developments, propose novel frameworks, and bridge theoretical innovations with real-world implementations. We invite papers on topics including, but not limited to, architectural design, biomedical diagnostics, autonomous navigation, satellite imagery analysis, augmented reality and efficient learning for large-scale/high-resolution images. We welcome theoretically rigorous and experimentally validated contributions that advance the frontier of structural networks in imaging. Papers may span algorithms, architectures, benchmarks, and interdisciplinary case studies.
Dr. Chunwei Tian
Dr. Shuai Wu
Dr. Jian-Fang Hu
Dr. Yinbo Yu
Dr. Xin Wang
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning,
- deep networks
- image processing
- optimization methods
- statistical methods
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