Symmetry and Asymmetry in Computer Vision and Pattern Recognition
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 18
Special Issue Editor
Special Issue Information
Dear Colleagues,
Symmetry and asymmetry are fundamental structural properties that widely exist in both natural and artificial systems. In the field of computer vision, they carry significant theoretical and practical value. Symmetry often manifests as geometric repetition, balance, or predictability, and can serve as a structural prior in deep neural networks to enhance generalization and robustness in tasks such as object detection, image segmentation, 3D reconstruction, pose estimation, and image generation. By contrast, asymmetry frequently reveals salient regions, structural abnormalities, or semantic discrepancies within visual data, and is widely applied in anomaly detection, change detection, face recognition, and medical image analysis. Recent advances increasingly integrate symmetry modeling with deep learning architectures to improve the structural understanding and interpretability of visual systems.
More broadly, in pattern recognition, symmetry and asymmetry are not limited to image data but are also found in non-visual modalities such as text, speech, time-series, and spectral signals. For example, in spectral analysis, the symmetry or asymmetry of absorption peaks in spectral curves may assist in the identification and classification of components in materials or quality detection. In sequential data like audio or physiological signals, symmetric/asymmetric patterns can support event detection and behavior modeling. In text analysis, symmetric structures in semantics can enhance contextual modeling in language models. Therefore, modeling such structural features in pattern recognition systems is vital for improving recognition accuracy, anomaly detection capabilities, and model interpretability.
This Special Issue aims to bring together the latest research advances on the modeling, analysis, and application of symmetry and asymmetry in computer vision and pattern recognition. We especially encourage submissions that explore the integration of deep learning, geometric modeling, and structural priors, and that promote the development of structure-aware intelligence across both visual and non-visual data modalities.
Dr. Li Yuan
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. 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
- symmetry
- asymmetry
- computer vision
- deep learning
- pattern recognition
- spectral analysis
- quality detection
- anomaly detection
- structural modeling
- multimodal fusion
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