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Journal of Discrete Mathematical Sciences and Cryptography cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Geometric and algebraic machine learning methods in computer graphics and vision systems

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pp. 895–904Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2540 Crossmark XML
Received:
09 Apr 2025
Published Online:
12 Feb 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2540
Pages:
895–904

Abstract

Computer vision and image processing are the subjects of this study, which investigates geometric and algebraic machine learning. Our ability to extract features and add data is facilitated by the use of vector measure spaces, manifolds, and algebraic structures such as groups and tensors. For the purpose of stabilizing and agreeing models, our technique makes use of orthogonal sets and sequences. Improvements in representations derived from high-dimensional visual input can be achieved by the application of techniques that combine geometric sensibility and mathematical rigour. The results of trials indicate that these tactics are effective in improving computer vision tasks such as object recognition and three-dimensional form analysis. 

Keywords

Subject Classifications

31A1033E30

References

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