Automated mineral identification algorithm using optical properties of crystals

Aligholi, Saeed; Khajavi, Reza; Razmara, Morteza

doi:10.1016/j.cageo.2015.09.014

Cited by 29 publications

(19 citation statements)

References 20 publications

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“…LBP has been extensively studied and has found many applications especially in facial image analysis (Ahonen et al 2006;Huang et al 2011). In relation to mineral texture analysis, LBP has been used, for example, to identify different mineral samples under microscopy (Aligholi et al 2015) and as a textural descriptor for drill core images (Koch et al 2019).…”

Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

Textural Quantification and Classification of Drill Cores for Geometallurgy: Moving Toward 3D with X-ray Microcomputed Tomography (µCT)

et al. 2020

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Texture is one of the critical parameters that affect the process behavior of ore minerals. Traditionally, texture has been described qualitatively, but recent works have shown the possibility to quantify mineral textures with the help of computer vision and digital image analysis. Most of these studies utilized 2D computer vision to evaluate mineral textures, which is limited by stereological error. On the other hand, the rapid development of X-ray microcomputed tomography (µCT) has opened up new possibilities for 3D texture analysis of ore samples. This study extends some of the 2D texture analysis methods, such as association indicator matrix (AIM) and local binary pattern (LBP) into 3D to get quantitative textural descriptors of drill core samples. The sensitivity of the methods to textural differences between drill cores is evaluated by classifying the drill cores into three textural classes using methods of machine learning classification, such as support vector machines and random forest. The study suggested that both AIM and LBP textural descriptors could be used for drill core classification with overall classification accuracy of 84–88%.

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Section: Local Binary Pattern (Lbp)mentioning

confidence: 99%

Textural Quantification and Classification of Drill Cores for Geometallurgy: Moving Toward 3D with X-ray Microcomputed Tomography (µCT)

et al. 2020

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“…But, the biggest disadvantage is that the research results of rock recognition cannot be applied to the field. And workers cannot use the research results of rock recognition to quickly and accurately identify the rock in the field [6,45,46]. is paper used ShuffleNet combined with the transfer learning method to train the rock recognition model.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones

Fan

Chen

et al. 2020

Mobile Information Systems

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In the geological survey, the recognition and classification of rock lithology are an important content. The recognition method based on rock thin section leads to long recognition period and high recognition cost, and the recognition accuracy cannot be guaranteed. Moreover, the above method cannot provide an effective solution in the field. As a communication device with multiple sensors, smartphones are carried by most geological survey workers. In this paper, a smartphone application based on the convolutional neural network is developed. In this application, the phone’s camera can be used to take photos of rocks. And the types and lithology of rocks can be quickly and accurately identified in a very short time. This paper proposed a method for quickly and accurately recognizing rock lithology in the field. Based on ShuffleNet, a lightweight convolutional neural network used in deep learning, combined with the transfer learning method, the recognition model of the rock image was established. The trained model was then deployed to the smartphone. A smartphone application for identifying rock lithology was designed and developed to verify its usability and accuracy. The research results showed that the accuracy of the recognition model in this paper was 97.65% on the verification data set of the PC. The accuracy of recognition on the test data set of the smartphone was 95.30%, among which the average recognition time of the single sheet was 786 milliseconds, the maximum value was 1,045 milliseconds, and the minimum value was 452 milliseconds. And the single-image accuracy above 96% accounted for 95% of the test data set. This paper presented a new solution for the rapid and accurate recognition of rock lithology in field geological surveys, which met the needs of geological survey personnel to quickly and accurately identify rock lithology in field operations.

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“…The field of ore mineralogy has traditionally used determination tables (Schouten, 1962;Uytenbogaardt and Burke, 1971;Spry and Gedlinske, 1987) that allow a reasonable approximation to the identification of minerals, although extensive experience is required in many cases in order to make a correct determination. In some fields of microscopy, increasingly sophisticated methods have been developed for determining the optical properties of minerals, as a punched card system (Fairbanks, 1946), optical identification of minerals in thin section by an interactive program (Reeves, 1989), multispectral imaging (Lane et al, 2008a), combination of bright field and circularly polarized light images (Gomes et al, 2013) or digital image analysis (Aligholi et al, 2015). To perform a reliable identification, analytical procedures have been implemented in the last decades based on multispectral (Bonifazi, 1995;Pirard, 2004;Lane et al, 2008aLane et al, , 2008bPirard et al, 2008), electron microscopy (Pirrie et al, 2004;Goodall et al, 2005;Pascoe et al, 2007) and/or computerized (Bernhardt, 1987;Glass and Voncken, 2010;Tonzetic et al, 2014) analyses, which in some cases require the presence of costly equipment.…”

Section: Introductionmentioning

confidence: 99%