Pattern recognition in computer vision using discrete structures and deep learning

Ashtagi, Rashmi; Kaulage, Anant; Pai, Anusha; Bidwe, Ranjeet Vasant; Rajput, Vaishali; Madagouda, Basavaraj

doi:10.47974/JDMSC-2511

Open Access Research Article

Pattern recognition in computer vision using discrete structures and deep learning

Rashmi Ashtagirashmiashtagi@gmail.comDepartment of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University KothrudPune, Maharashtra, 411038, IndiaView full profile → , Anant Kaulageanant.kaulage1@gmail.comDepartment of Computer Science and Engineering MIT Art, Design and Technology UniversityPune, Maharashtra, 412201, IndiaView full profile → , ^*Anusha PaiCorresponding authoranusha.pai@mitwpu.edu.inDepartment of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University KothrudPune, Maharashtra, 411038, IndiaView full profile → , Ranjeet Vasant Bidweranjeetbidwe@hotmail.comDepartment of Computer Science and Engineering Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune, Maharashtra, 412115, IndiaView full profile → , Vaishali Rajputvaishali.rajput@vit.eduDepartment of Artificial Intelligence and Data Science Vishwakarma Institute of TechnologyDepartment of Artificial Intelligence & Data Science Vishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Basavaraj Madagoudabasavarajmadagouda@gmail.comDepartment of Computer Science and Business Systems S. G. Balekundri Institute of TechnologyBelagavi, Karnataka, 590010, IndiaView full profile →

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pp. 681–689Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2511 Crossmark XML

Received:: 08 Apr 2025
Published Online:: 04 Feb 2026
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2511
Pages:: 681–689

Abstract

This work looks into ways to use deep learning and discrete mathematical structures together to make computer vision pattern recognition better. The suggested mixed models combine vector metric spaces, orthogonal sets, and discrete representations to make them better at finding features, being stable, and being easy to grasp. The technology uses novel data preparation tools and cutting-edge deep neural architectures that are based on discrete math’s to record intricate visual patterns. An in-depth architectural design and processing flow are shown, along with custom loss functions and optimization techniques that are used in training methods. It was tested and found that this way works better and needs fewer computers power than others.

Keywords

Pattern recognition Discrete structures Deep learning Computer vision Vector metric spaces

Subject Classifications

35B36

References

[1] J. Peng, C. Liu, H. Pang, X. Gao, G. Cheng, and B. Hao, “GP-Net: Image manipulation detection and localization via long-range modeling and transformers,” Appl. Sci., vol. 13, no. 12053 (2023).
[2] D. Dhabliya, J. R. R. Kumar, A. K. Dhablia, R. Dhabliya, S. Ikhar, V. Khetani, and A. Alkhayyat, “An Advantage Actor-Critic and Proximal Policy Optimization model for adaptive scheduling of heterogeneous cloud workloads,” Math. Model. Eng. Probl., vol. 11, no. 8, pp. 2275–2284 (2024).
[3] Tara Chandra, Mandeep Kaur, Nitin Rakesh, Monali Gulhane and Sudhanshu Maurya, “Novel blockchain-based framework to publish, verify, and store digital academic credentials of universities,” Int. J. Inf. Technol., vol. 16, pp. 3273–3281 (2024).
[4] S. Bhattacharya and M. Pandey, “Developing an agriculture ontology for extracting relationships from texts using Natural Language Processing to enhance semantic understanding,” Int. J. Inf. Technol. (2024).
[5] M. A. A. Sheela, G. Nikhitha, G. Chennakesavulu, D. M. Kumar, and K. M. Kumar, “Early stage detection of lung cancer using ensemble classification techniques,” Int. J. Adv. Comput. Eng. Commun. Technol. (IJACECT), vol. 14, no. 1, pp. 49–58 (Apr. 2025).
[6] H. J. Kang, “SSDLiteX: Enhancing SSDLite for small object detection,” Appl. Sci., vol. 13, no. 12001 (2023).
[7] S. Malwade, S. Choudhary, S. H. Lavate, P. N. Mahalle, S. N. Ajani, and V. Khetani, “Enhancing data security in transmission through quantum key distribution in cryptography,” in Proc. 2025 Int. Conf. Emerg. Smart Comput. Inform. (ESCI), Pune, India, pp. 1–6 (2025).
[8] Y. Zhao, L. Lu, W. Yang, Q. Li, and X. Zhang, “Lightweight tennis ball detection algorithm based on Robomaster EP,” Appl. Sci., vol. 13, no. 3461 (2023).
[9] B. Sun, X. Wang, A. Oad, A. Pervez, and F. Dong, “Automatic ship object detection model based on YOLOv4 with transformer mechanism in remote sensing images,” Appl. Sci., vol. 13, no. 2488 (2023).
[10] Z. Zhou, C. Zhou, A. Pan, F. Zhang, C. Dong, X. Liu, X. Zhai, and H. Wang, “A kitchen standard dress detection method based on the YOLOv5s embedded model,” Appl. Sci., vol. 13, no. 2213 (2023).
[11] P. Maxrizal, B. D. Aniska, Amri, and B. Adiwinoto, “New public-key cryptosystem algorithm based on function composition of matrix,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 6, pp. 1839–1849 (2024).

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