Classification of Objects by Shape Applied to Amber Gemstone Classification

• Main Authors: Armantas Ostreika, Marius Pivoras, Alfonsas Misevičius, Tomas Skersys, Linas Paulauskas
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Applied Sciences
• Subjects: image processing; computer vision; machine learning; expert systems; amber gemstones
• Online Access: https://www.mdpi.com/2076-3417/11/3/1024

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.