Guest Editors
Dr Lien Minh Dang
Email: minhdl@sejong.ac.kr
Affiliation: Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Republic of Korea
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Research Interests: Image processing, computer vision, pattern recognition, deep learning
Professor Hyeonjoon Moon
Email: hmoon@sejong.ac.kr
Affiliation: Computer Science & Engineering Department, Sejong University, Seoul, 05006, Republic of Korea
Homepage:
Research Interests: Image processing, big data, computer vision, pattern recognition, biometrics, artificial intelligence, deep learning
Summary
As computer vision technologies become increasingly integrated into various industries, the demand for more accurate, efficient, and innovative techniques is growing. This special issue focuses on novel methodologies that push the boundaries of image classification, object detection, and segmentation. It covers a wide range of topics, including but not limited to, deep learning architectures, data augmentation strategies, unsupervised and semi-supervised learning techniques, transfer learning, and explainable AI in visual recognition tasks.
Researchers and practitioners are invited to submit original contributions that present new models, algorithms, or systems that enhance the performance, scalability, and generalizability of image-based tasks. The issue also welcomes studies that address challenges such as handling noisy or imbalanced data, real-time processing, and applications across diverse domains like medical imaging, autonomous vehicles, and remote sensing. Through this special issue, we aim to provide a platform for sharing groundbreaking work that will shape the future of image analysis and drive forward the capabilities of computer vision systems.
Potential topics include, but are not limited to:
· Applications in Medical Imaging, Autonomous Vehicles, and Surveillance
· Novel Evaluation Metrics and Benchmarking
· Optimization Techniques for Improved Accuracy and Efficiency
· Emerging applications in robotics, agriculture, and environmental monitoring
Keywords
deep learning; convolutional neural networks (CNNs); unsupervised Learning; semi-supervised learning; explainable AI; real-time processing; remote sensing; visual recognition
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