Classification of Objects by Shape Applied to Amber Gemstone Classification

Author:

Ostreika ArmantasORCID,Pivoras Marius,Misevičius Alfonsas,Skersys Tomas,Paulauskas Linas

Abstract

To properly and quickly evaluate an object’s shape, in a manner that is suitable for real-time applications, a set of parameters has been created and the shape parametric description (SPD) has been elaborated. This solution is focused on the classification of amber gemstones according to shape. To improve the results obtained by SPD, the most popular machine learning classification algorithms were applied and tested. The proposed method (i.e., SPD) achieved the fastest classification, requiring the least computational resources, while providing an accuracy of approximately 80%. The best results were achieved when the SPD parameters were used in a feedforward neural network (FFNN), and an accuracy of 91.5% was obtained, while the time required for the computations remained in a range that is acceptable for real-time applications.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. GEMTELLIGENCE: Accelerating gemstone classification with deep learning;Communications Engineering;2024-08-20

2. Foundation of a new technique for geometric and non-geometric multi-shapes similarities degrees using boundary unfolding transformation with applications;Alexandria Engineering Journal;2024-06

3. Gemstone Classification Using Deep Convolutional Neural Network;Journal of The Institution of Engineers (India): Series B;2024-02-24

4. GemID: A Hybrid CNN-Random Forest Approach for Accurate Gemstone Identification;2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2023-12-29

5. The contribution of amber to heritage tourism development;Geographia Polonica;2023-11-13

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