Artificial Intelligence in Image and Video Processing Applications
A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Engineering".
Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 802
Special Issue Editors
Interests: biomedical; signal processing; image processing; video processing; artificial intelligence
Special Issue Information
Dear Colleagues,
Artificial intelligence is a multidisciplinary field with varied applications in many different fields. When the sub-branches of artificial intelligence are examined, it is observed that it mostly focuses on machine-learning- and deep-learning-based studies. In recent years, it has become possible for state-of-the-art deep learning-based artificial intelligence applications to be used for signal, image, and video processing in different disciplines. Engineering and healthcare stand out among these disciplines. Classification, segmentation, and detection studies are carried out with deep learning to help physicians, especially in the biomedical field. Likewise, there are various applications in the field of veterinary science and dentistry. As a result, artificial intelligence in image and video processing applications find their place in many different areas of our lives, especially in healthcare.
In this Special Issue, the goal is to publish research and review articles on “Artificial Intelligence in Image and Video Processing Applications”. Potential main topics include but are not limited to:
- Artificial intelligence in engineering, medicine, veterinary science, and dentistry;
- Signal, image, and video processing;
- Machine learning and deep learning applications in multidisciplinary fields.
Prof. Dr. Fırat Hardalaç
Dr. Fatih Uysal
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- deep learning
- signal processing
- image processing
- video processing
- classification
- segmentation
- object detection
- change detection
- transfer learning
- ensemble learning
- meta learning
