Automatic amber gemstones identification by color and shape visual properties

Sinkevicius, Saulius; Lipnickas, Arūnas; Rimkus, Kestas

doi:10.1016/j.engappai.2014.09.011

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…A comparison of the classification speed showed that the proposed SPD method is up to 11 times faster compared to the initially referenced method described in [6,7]. FFNN also showed good results in terms of timing, meaning it is applicable in real production processes, since it has an accuracy of more than 90%.…”

Section: Discussionmentioning

confidence: 89%

“…Some authors [6,7] have proposed amber classification according to the shape, based on contour properties. The algorithm begins with the processing of each photo.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Due to the relatively large amount of data needed to describe the form of amber, the algorithm is not suitable for real-time operating systems because it requires large computational resources. This approach [6,7] requires an expert who must first select amber forms, classify them by hand, and then train the decision trees. Therefore, in this investigation, we also asked ourselves, are there any methods that could help us avoid manual work in such classification tasks?…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Classification of Objects by Shape Applied to Amber Gemstone Classification

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 89%

“…Some authors [6,7] have proposed amber classification according to the shape, based on contour properties. The algorithm begins with the processing of each photo.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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Classification of Objects by Shape Applied to Amber Gemstone Classification

et al. 2021

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“…Proposed method is up to 11 times faster according to the methods described in publications [6] and [7].…”

Section: Discussionmentioning

confidence: 99%

“…Some authors propose [6,7] amber classification according to the shape, based on contour properties. The algorithm begins with the processing of each photo.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Classification of Amber Gemstone Objects by Shape

Ostreika¹,

Pivoras²,

Misevičius³

et al. 2020

Preprint

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The article describes a classification solution for amber stones. The problem of classifying amber is known for a long time among jewelers and artisans of amber art. Existing solutions can classify amber pieces according to color, but a need to classify by shape and texture is not satisfied up to now. The proposed solution is capable of classifying the gemstones according to a shape. Amber can be considered as a specific object since the form is difficult to define unambiguously. Data for amber experiments was gathered from amber art craftsmen. In the proposed solution amber form can be classified into 10 different classes (7 classes chosen during the experiment).

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Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset

Pena

Crabi

Izidoro

et al. 2021

Pattern Anal Applic

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The grading of gemstones is currently a manual procedure performed by gemologists. A popular approach uses reference stones, where those are visually inspected by specialists that decide which one of the available reference stone is the most similar to the inspected stone. This procedure is very subjective as different specialists may end up with different grading choices. This work proposes a complete framework that entails the image acquisition and goes up to the final stone categorization. The proposal is able to automate the entire process apart from including the stone in the created chamber for the image acquisition. It discards the subjective decisions made by specialists. This is the first work to propose a machine learning approach coupled with image processing techniques for emerald grading. The proposed framework achieves 98% of accuracy (correctly categorized stones), outperforming a deep learning approach. Furthermore, we also create and publish the used dataset that contains 192 images of emerald stones along with their extracted and pre-processed features.

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Automatic amber gemstones identification by color and shape visual properties

Cited by 9 publications

References 20 publications

Classification of Objects by Shape Applied to Amber Gemstone Classification

Classification of Objects by Shape Applied to Amber Gemstone Classification

Classification of Amber Gemstone Objects by Shape

Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset

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