AI-Driven Computer Vision and Pattern Recognition: Challenges and 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: 20 November 2025 | Viewed by 3961
Special Issue Editors
2. Department of Hydraulics and Sanitation, Technology Sector, Federal University of Paraná, Curitiba 81531-990, Brazil
Interests: computer vision; deep learning; Internet of Things (IoT); AI; big data; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: social computing; human-computer interaction; multimodal and natural language processing; user-centered interaction design
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, artificial intelligence has been revolutionizing the fields of computer vision and pattern recognition, driving significant advancements in various applications, including medical image analysis, autonomous systems, intelligent surveillance, and advanced robotics. However, integrating AI into these domains still presents numerous challenges, such as decision interpretability, model robustness, the need for high-quality labeled data, as well as ethical and security concerns.
This Special Issue is dedicated to exploring advanced solutions for AI-driven computer vision and pattern recognition, highlighting pioneering research, innovative methodologies and applications, as well as groundbreaking technologies that are advancing applications in these fields.
We particularly welcome contributions that advance the frontiers of these fields through novel algorithms, deep learning architectures, optimization strategies, practical applications, and interdisciplinary approaches. Submissions may address, but not limited to, the following topics:
- Advancements in computer vision and pattern recognition algorithms;
- Robustness and interpretability of AI models;
- AI for image and video processing in complex environments;
- Image generation and synthesis through deep generative models;
- Self-supervised learning and few-shot learning techniques;
- Applications of AI-driven computer vision and pattern recognition in critical domains, such as healthcare, security, Industry 4.0, autonomous systems;
- Ethical considerations and bias in AI-driven systems.
Prof. Dr. Heinz Dieter Fill
Dr. Arianna D'Ulizia
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- computer vision
- pattern recognition algorithms
- smart environments
- intelligent surveillance
- medical image analysis
- autonomous systems
- advanced robotics
- decision interpretability
- model robustness
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