METHODS FOR FINDING KEY POINTS IN AMBER SAMPLE IMAGES AS A BASIS FOR THREE-DIMENSIONAL MODELING

Authors

DOI:

https://doi.org/10.30890/2709-2313.2025-43-01-020

Keywords:

SFM, algorithm, SIFT, amber, triangulation, computer vision, camera calibration

Abstract

The paper considers an approach to three-dimensional visualization and modeling of amber samples to assess their shape, inclusions, color, and volume with subsequent industrial interpretation of the results. The obtained 3D data allow for the evaluation o

References

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Lobo T. Understanding Structure From Motion Algorithms. Medium. 18.12.2023. URL: https://medium.com/@loboateresa/understanding-structure-from-motion-algorithms-fc034875fd0c (дата звернення: 01.11.2025).

Timothy M. What is Scale-Invariant Feature Transform (SIFT)?. Roboflow. 20.09.2024. URL: https://blog.roboflow.com/sift/ (дата звернення: 04.11.2025).

BRIEF (Binary Robust Independent Elementary Features). Opencv. OpenCV-Python Tutorials. 17.06.2025. URL: https://docs.opencv.org/3.4/dc/d7d/ tutorial_py_brief.html (дата звернення: 07.11.2025).

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Published

2025-10-30

How to Cite

Ryzhuk, A. (2025). METHODS FOR FINDING KEY POINTS IN AMBER SAMPLE IMAGES AS A BASIS FOR THREE-DIMENSIONAL MODELING. European Science, 1(sge43-01), 74–85. https://doi.org/10.30890/2709-2313.2025-43-01-020

Issue

No. sge43-01 (2025): Intellectual and technological potential of the XXI century '2025

Section

Chapters

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Copyright (c) 2025 Authors

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