Innovative Computer Vision and Deep Learning Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 May 2026 | Viewed by 64
Special Issue Editor
Special Issue Information
Dear Colleagues,
The convergence of computer vision and deep learning has revolutionized how machines perceive and interpret visual information, enabling breakthrough applications across numerous domains. This Special Issue seeks to showcase cutting-edge research that advances both fundamental methodologies and practical implementations in visual intelligence systems.
We invite original research contributions that address the broad spectrum of computer vision challenges using state-of-the-art deep learning techniques. Topics of interest include visual recognition and understanding, 3D vision and scene reconstruction, video analysis and temporal reasoning, vision-language and multimodal learning, generative models for visual content, and applications across diverse domains such as healthcare, autonomous systems, robotics, industrial automation, agriculture, and remote sensing. We particularly welcome papers that present novel architectures, efficient algorithms for resource-constrained environments, and innovative solutions to real-world deployment challenges.
Dr. Boeun Kim
Guest Editor
Manuscript Submission Information
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Keywords
- autonomous driving and vehicle perception
- industrial computer vision and quality inspection
- medical image analysis and diagnosis
- security and safety in vision systems
- robotics vision and manipulation
- remote sensing and satellite imagery analysis
- sports analytics and player tracking
- agricultural computer vision applications
- face recognition and biometric systems
- intelligent surveillance and security
- augmented and virtual reality
- object detection, recognition, and tracking
- 3D vision and scene reconstruction
- image and video segmentation
- human pose estimation and action recognition
- video understanding and temporal modeling
- vision-language models and multimodal learning
- generative models and image synthesis
- real-time and edge computing for vision
- explainable AI in computer vision
- efficient neural network design and compression
- low-level vision and computational imaging
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