COMPUTER VISION APPROACH FOR PANORAMA CREATION IN MILITARY OBSERVATION

Authors

  • Krasimir Slavyanov Department Computer Systems and Technologies, Vasil Levski National Military University, Shumen, Bulgaria https://orcid.org/0000-0002-7234-5820

DOI:

https://doi.org/10.68302/std2026.vol3.153

Keywords:

computer vision, panorama creation, speeded-up robust features, transformation calculation

Abstract

In this paper the problem of obtaining a complete picture of monitored military objects is resolved with computer vision tools. The research is focused on the necessity for the correct arrangement of photos obtained in different ways to promptly obtain a comprehensive picture of the situation in an area of monitored military operation. The consecutive stages with functional operations are designed in MATLAB environment. Creation of a dataset with detailed images of specific zone of a military operation is described. Speeded-up robust features point detection is followed by descriptor extraction, transformation calculation and other specific computer vision tools. The functions for alpha blending and automatic black edge cropping are implemented for the final panorama creation.

Supporting Agencies

This work was supported by the National Science Program "Security and Defence", which has received funding from the Ministry of Education and Science of the Republic of Bulgaria under the grant agreement no. Д01-74/19.05.2022. The material reflects only the author's opinion, and the Ministry of Education and Science is not responsible for the content.

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References

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Published

17.09.2026

Issue

Vol. 3 (2026): Proceedings of the International Conference on Secure Systems Design and Technology Development.

Section

INFORMATION TECHNOLOGY

License

Copyright (c) 2026 Krasimir Slavyanov

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K. Slavyanov, “COMPUTER VISION APPROACH FOR PANORAMA CREATION IN MILITARY OBSERVATION”, SysTechDev, vol. 3, pp. 291–296, Sep. 2026, doi: 10.68302/std2026.vol3.153.