New Trends in Image Processing and Computer Vision
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 May 2027 | Viewed by 12
Editors
Interests: computer vision; image processing; medical image segmentation; active learning
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; object tracking; machine learning; self-supervised learning; active learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Image processing and computer vision are inherently interdisciplinary fields deeply rooted in mathematical foundations, including variational calculus, partial differential equations, optimization theory, probability and statistics, mathematical morphology, and geometric algebra. Recent years have witnessed remarkable progress driven by the integration of rigorous mathematical modeling and advanced learning paradigms, ranging from variational image analysis and sparse representation to foundation models and geometric deep learning. Meanwhile, emerging challenges concerning domain generalization, 3D scene understanding, multi-modal perception and embodied intelligence demand innovative mathematical theories and algorithmic frameworks.
This Special Issue will center on mathematical methodologies and their applications in image processing and computer vision, aiming to publish state-of-the-art research where mathematical insights facilitate methodological innovations. We welcome original research contributions covering, but not limited to, the following topics: foundation models for vision, embodied AI / VLA models, domain generalization, mathematical theory of deep learning, new theories of variational methods and PDEs for image analysis, applications of convex/non-convex optimization in visual inverse problems, mathematical methods in medical image analysis, mathematical morphology and optimization for industrial inspection, and mathematical methods and visual perception for autonomous driving.
Submissions with solid mathematical underpinnings oriented toward application scenarios including computer vision, medical imaging, remote sensing, industrial inspection and autonomous driving are highly encouraged. This Special Issue intends to promote interdisciplinary exchanges and integration between the communities of applied mathematics and computer vision.
Dr. Xiu Shu
Dr. Di Yuan
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics 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 2600 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
- foundation models for vision
- embodied AI / VLA models
- domain generalization
- mathematical theory of deep learning
- new theories of variational methods and PDEs for image analysis
- applications of convex/non-convex optimization in visual inverse problems
- mathematical methods in medical image analysis
- mathematical morphology and optimization for industrial inspection
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