Automatic classification of ornamental stones using Machine Learning techniques A study applied to limestone

Tereso, Marco; Rato, Luís; Gonçalves, Teresa

doi:10.23919/cisti49556.2020.9140872

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Testing of model showed the advantages of the methods over other de-noising methods; however more work is needed to improve its features. Classification system of ornamental rocks by using analysis and classification of images, based on machine learning algorithms was developed by Tereso et al 2020 [17]. This method will be used for quality control of different type of rocks in order to reduce material waste.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating Artificial Intelligence and Modern Technologies Enhancement in Stone and Marble Cutting in Palestine

2024

jjmie

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Stone and Marble sector in Palestine has a significant contribution to the GDP. There are some modern factories that use computerized and numerical controls for stone cutting employing modern tools and processes, but most of the marble cutting factories depend on manual and small machinery production lines. This paper examines integrating modern technologies; artificial intelligence (AI) and mechatronic systems like sensors, actuators and control systems, in these cutting factories in order to increase their efficiency, improve the added value and contribute in the occupational safety for human labor and cutting processes. In particular artificial intelligence, machine learning and image processing tools will be investigated as examples of modern technologies that can be implemented in marble and stone cutting processes in Palestine. Four Convolutional Natural Network (CNN) types were employed to classify marble slabs, comparing colored and gray-level databases. Gray-level yielded superior recognition rates due to marble's prevalent gray color. Texture, not color, drove classification; ResNet-152 achieved 100% Recognition Rate (RR) for gray-level and 98.6% for colored. In terms of efficiency, Inception CNN excelled. Ultimately, gray-level images best served marble classification, rendering color irrelevant.

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Section: Literature Reviewmentioning

confidence: 99%

Investigating Artificial Intelligence and Modern Technologies Enhancement in Stone and Marble Cutting in Palestine

2024

jjmie

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“…Other literature works that use the DL approach for automatic stone classification is [33], where automatic recognition and classification of granite tiles is the object of study using CNN networks such as AlexNet and VGGNet for a fine-tuning pre-trained approach, or [34] where the authors implement a classification model of ornamental rocks through the analysis and classification of images, using machine learning algorithms.…”

Section: Stone Classificationmentioning

confidence: 99%

Automatic Stones Classification through a CNN-Based Approach

Tropea

Fedele

Luca

et al. 2022

Sensors

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This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Identificazione di Lapidei Per Immagini”), financed by POR Calabria FESR-FSE 2014-2020. Our study is based on the Convolutional Neural Network (CNNs) that is used in literature for many different tasks such as speech recognition, neural language processing, bioinformatics, image classification and much more. In particular, we propose a two-stage hybrid approach based on the use of a model of Deep Learning (DL), in our case the CNN, in the first stage and a model of Machine Learning (ML) in the second one. In this work, we discuss a possible solution to stones classification which uses a CNN for the feature extraction phase and the Softmax or Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB) ML techniques in order to perform the classification phase basing our study on the approach called Transfer Learning (TL). We show the image acquisition process in order to collect adequate information for creating an opportune database of the stone typologies present in the Calabrian quarries, also performing the identification of quarries in the considered region. Finally, we show a comparison of different DL and ML combinations in our Two-Stage Hybrid Model solution.

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Machine Learning Techniques to Detect Heart Disease in Early Stage Using Feature-Based Classification

Rajak,

Kumar,

Panda

et al. 2023

Algorithms for Intelligent Systems

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Automatic classification of ornamental stones using Machine Learning techniques A study applied to limestone

Cited by 7 publications

References 10 publications

Investigating Artificial Intelligence and Modern Technologies Enhancement in Stone and Marble Cutting in Palestine

Investigating Artificial Intelligence and Modern Technologies Enhancement in Stone and Marble Cutting in Palestine

Automatic Stones Classification through a CNN-Based Approach

Machine Learning Techniques to Detect Heart Disease in Early Stage Using Feature-Based Classification

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