Advanced Machine Learning Methods for Image Processing, Perception and Understanding
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".
Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 17376
Special Issue Editors
Interests: image processing; machine learning; video analysis
Special Issue Information
Dear Colleagues,
Image processing, perception and understanding have underpinned much of recent progress in a wide range of computer vision applications, including space exploration, military surveillance, environmental protection, precise agriculture, intelligence manufacturing, intelligent transportation etc. However, due the complicated imaging environments as well as various application requirements in practice, image processing, perception and understanding are still confronted with many challenges, including image quality degeneration, multi-source image fusion, few-shot learning, cross-domain generalization, unsupervised learning, fast inference, embedded employment etc. Thus, it is necessary to investigate advanced machine learning methods to mitigate these existing challenges, and to sufficiently exploit the potential of image processing, perception and understanding in more real-world applications.
This Special Issue calls for innovative works that explore recent advances, prospects, and challenges in machine learning methods or applications to produce high-quality images; improve the generalization performance towards different perception and understanding tasks under degenerated images, few-shot annotations, cross-domain data etc.; as well as accelerate the inference speed, especially on embedded devices with limited computational resources. It is noteworthy that in this Special Issue the keyword ‘image’ must be understood in a wide sense: optical imagery, infrared imagery, multispectral imagery, hyperspectral imagery, SAR imagery, medical imagery, and so on. The purpose is to provide a platform to enhance interdisciplinary research and collaborations, and to share the most innovative ideas in various related fields.
Prof. Dr. Lei Zhang
Prof. Dr. Wei Wei
Guest Editors
Manuscript Submission Information
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Keywords
- image restoration and fusion methods
- image classification and segmentation methods
- object detection and recognition methods
- conventional machine learning methods
- deep learning methods