Symmetry in Aerospace Image Detection and Target Tracking
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".
Deadline for manuscript submissions: 31 October 2024 | Viewed by 208
Special Issue Editors
Interests: machine learning; aerospace systems; autonomous unmanned systems; visual tracking; deep learning; pose estimation; object detection; information fusion; swarm intelligence; robotics
Special Issues, Collections and Topics in MDPI journals
Interests: Correlation Filter; Computer Vision; Multiple Object Tracking
Special Issue Information
Dear Colleagues,
Aerospace image detection and target tracking have commonly enjoyed leveraging and incorporating techniques from the wider field of image processing and computer vision. Compared to generic image photography, aerospace images often present relatively lower resolution, orientation, and turbulent disturbance. In many fields, recent deep learning approaches have shown great success and had a remarkable impact, especially thanks to the availability of large, annotated benchmark datasets. Their effects in aerospace image detection and target tracking are also prominent, although the lack of large, annotated datasets, may pose limitations.
In this Special Issue, we aim to cover recent advances and applications of symmetry in aerospace image detection and target tracking. We are particularly interested in exploring novel applications of deep learning approaches, although submissions are open to a wider range of aerospace image processing topics. Some potential areas of interest include methods for efficient approaches to annotations, dealing with a low number of annotations, and approaches to deal with image sequences.
We welcome submissions on topics including, but not limited to, the following: Novel applications of deep or machine learning. Applications of symmetry in aerospace detection and tracking. Applications in aerospace image detection, target tracking and others. Applications in different aerospace image modalities, including natural language, depth, thermal, and so on.
Dr. Yong Wang
Dr. Lingkun Luo
Dr. Xian Wei
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. Symmetry is an international peer-reviewed open access monthly 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
- machine learning
- aerospace image detection
- target tracking
