A computer vision system for identification of granite-forming minerals based on RGB data and artificial neural networks

Ramil, A.; Díaz, Ana Jesús López; Pozo-António, J.S.; Rivas, T.

doi:10.1016/j.measurement.2017.12.006

Cited by 35 publications

(20 citation statements)

References 17 publications

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“…The results are presented for 8, 16, and 32 training samples, respectively ( Table 5, Table 6, and Table 7). A computer vision system was addressed in [53] for identifying the constituents of granite in an RGB space using artificial neural networks. The study also addresses the laser cleansing of granite stoneworks.…”

Section: Resultsmentioning

confidence: 99%

Sorting Speckled Granites (Nehbandan) and Measuring Their Surface Veining using Machine Vision

Moghaddam*¹,

Rajaei²,

Maraki³

2019

IJRTE

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Quality control and the appearance evaluation of stones are quite challenging in the industry today. The similar appearance of different stones containing the same minerals may result in economic losses, and if the customers fail to identify the type of slates delivered to them correctly, disagreements may arise between the buyers and granite vendors. This study is an attempt toward the automation of surface quality assessment of the Nehbandan(Iran) speckled granite and measurement of the surface patterns under fixed conditions using image processing techniques in order to classify the granite tiles based on their type and amount of impurities and veins. The experimental tests comparing the presented approach with the texture descriptors in the introduced dataset prove the efficiency of the proposed method and its applications under controlled conditions, including the classification of speckled granite tiles with different image resolutions.

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Section: Resultsmentioning

confidence: 99%

Sorting Speckled Granites (Nehbandan) and Measuring Their Surface Veining using Machine Vision

Moghaddam*¹,

Rajaei²,

Maraki³

2019

IJRTE

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“…As geological surveys, resource companies and research institutes are increasingly digitizing petrographic thin sections, approaches for an automated quantitative analysis are now becoming a standard tool to investigate properties in thin sections (e.g. Marmo et al, 2005;Singh et al, 2010;M lynarczuk et al, 2013;Thompson et al, 2001;Baykan and Yılmaz, 2010;Borges and de Aguiar, 2019;Ramil et al, 2018;Maitre et al, 2019). These approaches have been driven by the rapid developments in the field of visual image analysis and segmentation leading to considerable progress in computer-aided methods for the automated analysis of mineral thin section images.…”

Section: Introductionmentioning

confidence: 99%

Superpixel segmentations for thin sections: evaluation of methods to enable the generation of machine learning training data sets

Yu¹,

Wellmann²,

Virgo³

et al. 2021

Preprint

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Training data is the backbone of developing either Machine Learning (ML) models or specific deep learning algorithms. The paucity of well-labeled training image data has significantly impeded the applications of ML-based approaches, especially the development of novel Deep Learning (DL) methods like Convolutional Neural Networks (CNNs) in mineral thin section images identification. However, image annotation, especially pixel-wise annotation is always a costly process. Manually creating dense semantic labels for rock thin section images has been long considered as an unprecedented challenge in view of the ubiquitous variety and complexity of minerals in thin sections. To speed up the annotation, we propose a human-computer collaborative pipeline in which superpixel segmentation is used as a boundary extractor to avoid hand delineation of instances boundaries. The pipeline consists of two steps: superpixel segmentation using MultiSLIC, and superpixel labeling through a specific-designed tool. We use a cutting-edge methodology Virtual Petroscopy (ViP) for automatic image acquisition. Bentheimer sandstone sample is used to conduct performance testing of the pipeline. Three standard error metrics are used to evaluate the performance of MultiSLIC. The result indicates that MultiSLIC is able to extract compact superpixels with satisfying boundary adherence given multiple input images. According to our test results, large and complex thin section images with pixel-wisely accurate labels can be annotated with the labeling tool more efficiently than in a conventional, purely manual work, and generate data of high quality.

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“…The main advantage of ANNs is their ability to generalize or learn from examples 17 ; that is, ANNs can generalize learned information to provide satisfactory results for cases not seen in training. Therefore, ANNs have been used in many fields, such as chemistry 18 , geology 19 , medicine 20 , neurocomputations 21 and biomedical engineering 22 , among others.…”

Section: Introductionmentioning

confidence: 99%

Estimating bulk density in leguminous grains with different traits using color parameters from digital images combined with artificial neural networks

2020

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Dry grains from leguminous species, such as soybeans (Glycine max L.), common beans (Phaseolus vulgaris L.), chickpeas (Cicer arietinum L.) and corn (Zea mays L.), are regularly consumed for human nutrition. This paper showed the possibility of estimating bulk density as quality parameter of 4 different dry grains (soybeans, common beans, chickpeas and corn) in a same model using the average values of color descriptors from digital images combined with an artificial neural network, with low computational costs. These food products are good sources of carbohydrates, protein and dietary fiber, and they possess significant amounts of vitamins and minerals and a high energetic value. Estimation of the physicochemical properties of grains is challenging due to variations in shape, texture, and size and because the grain colors appear similar to the naked eye. In this work, an analytical method was developed based on digital images converted into ten color scale descriptors combined with a neural model to provide an accurate parameter for grain quality control with a low computational cost. The bulk densities of four type of grains, i.e., soybeans, beans, chickpeas and corn, were predicted using numerical data represented by the average values of color histograms of a ten color scale (red - R, green - G, blue - B, hue - H, saturation - S, value - V, relative RGB and luminosity - L) from digital images combined with artificial neural networks (ANNs). The reference bulk densities were empirically measured. A very good correlation between the reference values and values predicted by the ANN was achieved, and with a single ANN developed for the four grains, a correlation coefficient of 0.98 was observed for the test set. Moreover, the relative errors were between 0.01 and 5.6% for the test set.

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A computer vision system for identification of granite-forming minerals based on RGB data and artificial neural networks

Cited by 35 publications

References 17 publications

Sorting Speckled Granites (Nehbandan) and Measuring Their Surface Veining using Machine Vision

Sorting Speckled Granites (Nehbandan) and Measuring Their Surface Veining using Machine Vision

Superpixel segmentations for thin sections: evaluation of methods to enable the generation of machine learning training data sets

Estimating bulk density in leguminous grains with different traits using color parameters from digital images combined with artificial neural networks

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