Submission Deadline: 28 February 2026 View: 1966 Submit to Special Issue
Prof. Dr. Guanqiu Qi
Email: qig@buffalostate.edu
Affiliation: Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, 14222, USA
Research Interests: computer vision, deep learning
Prof. Dr. Zhiqin Zhu
Email: zhuzq@cqupt.edu.cn
Affiliation: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
Research Interests: computer vision, deep learning
With the rapid development of the field of computer vision, image segmentation and object detection technologies are playing an increasingly important role in various application scenarios. In recent years, the introduction of biomimetic computing methods has provided new perspectives and solutions for the advancement of these technologies. These methods draw on the characteristics of biological phenomena and systems, such as visual perception, the functioning of neural networks, and swarm intelligence, bringing innovative ideas to image segmentation and object detection.
This special issue aims to gather the latest research findings and applications of biomimetic computing methods in image segmentation and object detection, encouraging both theory-driven and application-oriented research, particularly those papers that demonstrate novelty in technical depth and engineering applications.
Potential topics include, but are not limited to the following:
· 3D from multi-view and sensors
· 3D from single images
· Autonomous driving
· Computational imaging
· Computer vision for robotics
· Computer vision theory
· Datasets and evaluation
· Deep learning architectures and techniques
· Embodied vision: Active agents, simulation
· Event-based cameras
· Explainable computer vision
· Humans: Face, body, pose, gesture, movement
· Image and video synthesis and generation
· Machine learning (other than deep learning)
· Medical and biological vision, cell microscopy
· Multimodal learning
· Optimization methods (other than deep learning)
· Photogrammetry and remote sensing
· Physics-based vision and shape-from-X
· Recognition: Categorization, detection, retrieval
· Representation learning
· Scene analysis and understanding
· Segmentation, grouping and shape analysis
· Self-, semi-, meta- and unsupervised learning
· Transfer/ low-shot/ continual/ long-tail learning
· Video: Action and event understanding
· Video: Low-level analysis, motion, and tracking
· Vision + graphics
· Vision, language, and reasoning
· Vision applications and systems
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