Classification of Objects by Shape Applied to Amber Gemstone Classification

被引：2

作者：

Ostreika, Armantas ^{[1
]}

Pivoras, Marius ^{[1
]}

Misevicius, Alfonsas ^{[1
]}

Skersys, Tomas ^{[1
]}

Paulauskas, Linas ^{[2
]}

机构：

[1] Kaunas Univ Technol, Fac Informat, Studentu Str 50, LT-51368 Kaunas, Lithuania

[2] MB Supakis, K Donelaicio G 20, LT-44239 Kaunas, Lithuania

来源：

APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 03期

关键词：

image processing; computer vision; machine learning; expert systems; amber gemstones;

D O I：

10.3390/app11031024

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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.

引用

页码：1 / 14

页数：14

共 13 条

[1] [Anonymous], 2010, P 6 INT C SIGN PROC
[2] Bochkarev S.O., IRREGULAR OBJECTS SH
[3] Jorge J., SIMPLE APPROACH RECO
[4] Simplified Classification of Multispectral Image Fragments
Lorencs, A.
Mednieks, I.
Sinica-Sinavskis, J.
[J]. ELEKTRONIKA IR ELEKTROTECHNIKA, 2014, 20 (06) : 136 - 139
[5] Convolutional neural networks for recognition and segmentation of aluminium profiles
Mazzeo, Pier Luigi
Argentieri, Arturo
De Luca, Federico
Spagnolo, Paolo
Distante, Cosimo
Leo, Marco
Carcagni, Pierluigi
[J]. MULTIMODAL SENSING: TECHNOLOGIES AND APPLICATIONS, 2019, 11059
[6] Design of a system that uses optical-fiber sensors and neural networks to control a large-scale industrial oven by monitoring the food quality online
O'Farrell, M
Lewis, E
Flanagan, C
Lyons, WB
Jackman, N
[J]. IEEE SENSORS JOURNAL, 2005, 5 (06) : 1407 - 1420
[7] Pearson T., 2012, Journal of Food Measurement and Characterization, V6, P27
[8] Color image based sorter for separating red and white wheat
Pearson T.
Brabec D.
Haley S.
[J]. Sensing and Instrumentation for Food Quality and Safety, 2008, 2 (04): : 280 - 288
[9] Rimkus K., 2013, P IEEE 7 INT C INT D, DOI [10.1109/IDAACS.2013.6662694, DOI 10.1109/IDAACS.2013.6662694]
[10] Sarstedt M., 2014, CONCISE GUIDE MARKET, V2nd, DOI [10.1007/978-3-642-53965-7978-3-642-53965-7, DOI 10.1007/978-3-642-53965-7978-3-642-53965-7]

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