Deadline Date: 30 June 2027
Computer vision and pattern recognition stand at the forefront of artificial intelligence, integrating multidisciplinary advances in machine learning, signal processing, and mathematical theory. In recent years, deep learning techniques—particularly convolutional neural networks (CNNs)—have dramatically reshaped the landscape of these fields. CNNs excel at automatically learning discriminative feature representations directly from raw pixel data, enabling unprecedented performance across tasks such as object recognition, semantic segmentation, image synthesis, and scene interpretation. The growing ubiquity of vision-based systems in real-world applications underscores the increasing importance of innovative research in this area.
This Special Issue aims to compile cutting-edge research advances and practical applications within computer vision and pattern recognition. We seek to highlight novel methodologies, systematic reviews, and impactful case studies that reflect the latest trends and solutions. The issue will provide a platform for researchers and practitioners to share insights that bridge theoretical innovation and real-world deployment, fostering further development in intelligent visual understanding.
Suggested Topics:
We invite original contributions including, but not limited to, the following themes:
Object detection, recognition, and tracking
Image and video segmentation
Deep representation learning and feature encoding
Generative models for image synthesis and enhancement
Scene understanding and semantic interpretation
Large-scale visual recognition and retrieval
Multimedia information processing and analysis
Interdisciplinary applications of computer vision in healthcare, robotics, surveillance, and remote sensing
Submissions should present innovative ideas, solid empirical validation, and potential for broad impact within and beyond the research community.
Editorial board
