Assessment of chromite liberation spectrum on microscopic images by means of a supervised image classification

Camalan, Mahmut; Çavur, Mahmut; Hoşten, Çetin

doi:10.1016/j.powtec.2017.08.063

Cited by 11 publications

(12 citation statements)

References 43 publications

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“…Automatic geological identification is becoming an increasingly important technique in various fields, such as in mining and geology. Camalan et al presented a novel strategy to estimate the liberation spectrum from optical micrographs via random forest [8]. Lei et al proposed an autonomous classification method of rock images via unsupervised feature learning [9].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

et al. 2019

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Featured Application: Maceral Analysis; Coal Processing.Abstract: An intelligent analytical technique which is able to accurately identify maceral components is highly desired in the fields of mining and geology. However, currently available methods based on fixed-size window neglect the shape information, and thus do not work in identifying maceral composition from one entire photomicrograph. To address these concerns, we propose a novel Maceral Identification strategy based on image Segmentation and Classification (MISC). Considering the complex and heterogeneous nature of coal, a two-level coarse-to-fine clustering method based on K-means is employed to divide microscopic images into a sequence of regions with similar attributes (i.e., binder, vitrinite, liptinite and inertinite). Furthermore, comprehensive features along with random forest are utilized to automatically classify binder and seven types of maceral components, including vitrinite, fusinite, semifusinite, cutinite, sporinite, inertodetrinite and micrinite. Evaluations on 39 microscopic images show that the proposed method achieves the state-of-the-art accuracy of 90.44% and serves as the baseline for future research on maceral analysis. In addition, to support the decisions of petrologists during maceral analysis, we developed a standalone software, which is freely available at https:/github.com/GuyooGu/MISC-Master.

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Section: Related Workmentioning

confidence: 99%

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

et al. 2019

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“…Table 3 shows that all the accuracy indicators including the producer and user accuracies for the objects are quite high as well as the overall accuracy and kappa statistics (Section 3.5), proving that the classifier works accurately for all the minerals and epoxy. The high accuracy for the background (epoxy) classification will indicate a high success on the background extraction that cannot be accomplished on oremicroscopy images [26]. Table 3 also shows that the producer accuracy of olivine is slightly lower than the accuracies of other minerals, because of the misclassification of olivine as serpentine.…”

Section: Resultsmentioning

confidence: 99%

“…Then watershed transformation [45] was applied to the classified BSE images (a sample is provided in Figure 5a) to separate particles erroneously connected by the classification (Figure 5b). The accuracy of the classification method was determined with the confusion matrix [26,46,47]. The confusion matrix included a comparison between the true and predicted compositions of a total of 1000 random points on the training images ( Figure 3).…”

Section: Experimental Materials and Proceduresmentioning

confidence: 99%

“…This observation suggests that the RFT is a prospective ML algorithm to classify BSE images of minerals of similar density, as long as sufficiently large numbers of training images and features are supplied to the RFT. Random forest is also expected to discriminate background (epoxy) from particles in BSE images, which had not been accomplished previously by using the same classification method on microscopic images [26]. As the RFT is embedded in user-friendly software, the users do not have to tackle the details of thresholding or other image processing methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a supervised classification method to construct 2D mineral mapson backscattered electron images

Camalan¹,

Çavur²

2020

Turk J Elec Eng & Comp Sci

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The Mineral Liberation Analyzer (MLA) can be used to obtain mineral maps from backscattered electron (BSE) images of particles. This paper proposes an alternative methodology that includes random forest classification, a prospective machine learning algorithm, to develop mineral maps from BSE images. The results show that the overall accuracy and kappa statistic of the proposed method are 97% and 0.94, respectively, proving that random forest classification is accurate. The accuracy indicators also suggest that the proposed method may be applied to classify minerals with similar appearances under BSE imaging. Meanwhile, random forest predicts fewer middling particles with binary and ternary composition, but the MLA predicts more middling particles only with ternary composition. These discrepancies may arise because the MLA, unlike random forest, may also measure the elemental compositions of mineral surfaces below the polished section.

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“…However, the frequent use of the polished sections does not ensure that such mineralogical analyses will always give the correct results. If the minerals in a particulate sample have different densities, relatively heavier minerals will preferentially settle to the polished section [10,[13][14][15] This phenomenon results in a 2D map from which the amount of heavier minerals is overestimated in sample particles [16]. The serial sectioning experiments made by Lätti and Adair [17] even suggested that this stereological error could disappear for a particulate sample that contained silicate minerals with similar densities.…”

Section: Using Random Forest Tree Classification For Evaluating Vertical Cross-sections In Epoxy Blocks To Get Unbiased Estimates For 3d mentioning

confidence: 99%

Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map

Camalan

Çavur²

2021

Politeknik Dergisi

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Assessment of chromite liberation spectrum on microscopic images by means of a supervised image classification

Cited by 11 publications

References 43 publications

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

Development of a supervised classification method to construct 2D mineral mapson backscattered electron images

Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map

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