Advances in Visual Systems for Pattern Recognition
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 24 July 2024 | Viewed by 170
Special Issue Editor
Interests: computer vision; machine learning; pattern recognition; visual transformers; deep learning; artificial intelligence; Internet of Things; automatic video surveillance; automatic inspection; robotics; automotive; sport event analysis; biometric people recognition; ambient assistive living; multimodal human–machine interfaces; human activity recognition; object recognition; object tracking; assistive technology
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Special Issue Information
Dear Colleagues,
The intersection of cutting-edge technology for machine learning and visual systems has ushered in a new era marked by unprecedented advancements in pattern recognition. It is a burgeoning research area that leverages the capabilities of visual systems, encompassing computer vision, machine learning, and artificial intelligence, to unravel complex patterns embedded in diverse data sources. Visual systems have evolved beyond mere image processing, now delving into intricate pattern recognition tasks with remarkable precision and efficiency. Follow-up technologies stand at the forefront of innovation and artificial intelligence applications and are making changes in several real-life contexts, such as medicine, the automotive industry, assistive technologies, robotics, sports events analysis, humanities, cultural heritage conservation, manufacturing, surveillance, and biometrics.
This Special Issue aims to bring together cutting-edge research and developments in visual systems for pattern recognition. We invite researchers and practitioners to contribute original and high-quality papers that address emerging challenges, propose novel solutions, and showcase innovative applications.
Papers are solicited in, but not limited to, the following areas:
- Deep learning approaches for visual pattern recognition;
- Transfer learning and domain adaptation in visual systems;
- Explainable AI in visual pattern recognition;
- Fusion of multimodal information for improved recognition;
- Real-time and scalable visual recognition systems;
- Robustness and adversarial attacks in visual pattern recognition;
- Novel datasets and benchmarking in visual recognition;
- Human–computer interaction in visual systems;
- Applications of visual pattern recognition in diverse domains (e.g., healthcare, finance, security).
Dr. Marco Leo
Guest Editor
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
- visual systems
- pattern recognition
- computer vision
- machine learning
- deep learning
- artificial intelligence
- embedded system
- image processing
- visual recognition
- human–computer interaction
