Novel Research on Exploration and Application of Intelligent Computer Vision Technology
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 June 2026
Special Issue Editors
Interests: image processing; computer vision; intelligent system
Interests: pattern recognition; image analysis; multi-band infrared image fusion; image signal processor; high-speed parallel processing technology; programmable device
Special Issue Information
Dear Colleagues,
In recent years, the convergence of artificial intelligence and computer vision has precipitated a paradigm shift in how machines perceive, interpret, and interact with the physical world. No longer confined to controlled laboratory environments, intelligent vision systems are now integral to solving complex problems in applied sciences. This Special Issue, titled "Novel Research on Exploration and Application of Intelligent Computer Vision Technology," aims to capture the cutting-edge developments driving this transformation, bridging the critical gap between theoretical algorithms and robust, real-world deployment.
We invite researchers and practitioners to submit contributions that explore the frontiers of novel computer vision technologies, including deep learning, intelligent sensing computing, advanced image processing, etc. The scope of this collection is broad yet focused on practical utility. We are particularly interested in novel computer vision technology, machine learning paradigms (continual learning/life-long learning, prompt learning, federal learning, etc.) in computer vision applications, multimodal data fusion, and real-time edge computing solutions that enhance system adaptability.
Key areas of interest include, but are not limited to, low-altitude economies, autonomous robotics and navigation, high-precision medical imaging for diagnostics, industrial defect detection (Industry 4.0), smart city surveillance, and remote sensing. Furthermore, we encourage studies addressing inherent challenges such as learning from scarce data, handling environmental noise, and optimizing computational efficiency in resource-constrained devices. By gathering high-quality research on these topics, this Special Issue seeks to provide a comprehensive outlook on the future landscape of intelligent computer vision and its profound impact on modern society.
Dr. Xu Zou
Prof. Dr. Sheng Zhong
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 250 words) can be sent to the Editorial Office for assessment.
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
- machine learning paradigm
- image processing
- multi-modal data fusion
- computer vision-based navigation
- medical image analysis
- defect detection
- remote sensing
- real-time computing
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