Advances in Computer Vision and Machine Learning
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 46582
Special Issue Editors
Interests: remote sensing scene classification; cross-domain scene classification
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; machine learning; big data analytics; hyperspectral imaging; nondestructive inspection
Special Issues, Collections and Topics in MDPI journals
Interests: Information and communication engineering; satellite communication and satellite navigation; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Computer vision focuses on the theories and practices that give rise to semantically meaningful interpretations of the visual world. Mathematical models and tools can provide enormous opportunities for developing intelligent algorithms that extract useful information from visual data, such as a single image, a video sequence, and even a multi-/hyper-spectral image cube. In recent years, a number of emerging machine learning techniques have been applied in visual perception tasks such as camera imaging geometry, camera calibration, image stabilization, multiview geometry, feature learning, image classification, and object recognition and tracking. However, it is still challenging to provide theoretical explanations of the underlying learning processes, especial when using deep neural networks, where a few questions remain to be answered, such as the design principles, the optimal architecture, the number of required layers, the sample complexity, and the optimization algorithms.
This Special Issue focuses on recent advances in computer vision and machine learning. The topics of interest include, but are not limited to, the following:
- Pattern recognition and machine learning for computer vision;
- Feature learning for computer vision;
- Self-supervised/weakly supervised/unsupervised learning;
- Image processing and analysis;
- Deep neural networks in computer vision;
- Graph neural networks;
- Optimization method for machine learning;
- Evolutionary computation and optimization problems;
- Emerging applications.
Dr. Xiangtao Zheng
Prof. Dr. Jinchang Ren
Prof. Dr. Ling Wang
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 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. 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
- artificial intelligence
- computer vision
- pattern recognition
- statistical learning
- data mining
- deep learning
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