Computer Vision and AI Algorithms for Diverse Scenarios
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 522
Special Issue Editors
Interests: crack detection; image recognition; multi-scale CNN extraction; high-resolution UAV images; machine learning; deep learning; data mining; performance evaluation of construction; construction risk management
Interests: fuzzy logic; linguistic algorithms; natural language processing; military domain knowledge discovering; soft computing; risk assessment; supply chain; reliability
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Special Issue Information
Dear Colleagues,
In recent years, the field of computer vision has undergone transformative advancements, driven largely by the rapid evolution of artificial intelligence (AI) algorithms. As these technologies continue to evolve, there is a pressing need to explore and refine AI-driven approaches to address emerging challenges and seize opportunities in computer vision. The rapid advancement of AI technologies and their application to computer vision have significantly enhanced our ability to interpret, analyze, and understand complex visual data across various domains.
This Special Issue aims to highlight cutting-edge research and developments in computer vision and AI algorithms tailored to a broad range of scenarios. Advances in deep learning techniques, including convolutional neural networks (CNNs), transformer models, and hybrid architectures, have significantly enhanced our ability to analyze and interpret visual data. These innovations have far-reaching implications across various domains, such as autonomous systems, healthcare, environmental monitoring, and more.
We encourage researchers and practitioners to contribute original research articles, review papers, and technical notes that address these topics, as this Special Issue aims to showcase state-of-the-art methodologies and applications in computer vision that leverage AI to tackle the complexities of diverse scenarios. By advancing the field of computer vision and AI algorithms, we aim to foster innovation and drive progress across a wide range of applications and scenarios. As such, we invite contributions that explore novel approaches, methodologies, and applications in areas including but not limited to the following:
- Advanced AI algorithms for object detection and recognition—techniques and models for analyzing and interpreting visual data across different environments;
- AI-Driven Image and Video Analysis—advances in algorithms for object detection, image segmentation, and action recognition using AI techniques;
- Adaptive AI Models for Varied Contexts—development of robust computer vision systems for real-time processing and analysis;
- Benchmark Datasets and Evaluation Metrics—benchmarking and evaluation of AI-driven computer vision models on diverse datasets;
- Cross-Domain Applications of Computer Vision—exploration of computer vision applications across different domains, including autonomous systems, security surveillance, healthcare, transportation, agriculture, and environmental monitoring;
- Integration of Multimodal Data—approaches that combine visual data with other modalities, such as textual or sensor data, to enhance understanding and analysis in diverse scenarios.
Dr. Ching-Lung Fan
Prof. Dr. Kuei-Hu Chang
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- computer vision
- image classification
- object detection
- semantic segmentation
- data-driven methods
- artificial intelligence
- linguistic algorithms
- natural language processing
- soft computing
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