A computer program (TSecSoft) to determine mineral percentages using photographs obtained from thin sections

Yesiloglu-Gultekin, N.; Keçeli, Ali Seydi; Sezer, Ebru Akçapınar; Can, Ahmet Burak; Gökçeoğlu, Candan; Bayhan, Hasan Alı

doi:10.1016/j.cageo.2012.01.001

Cited by 40 publications

(17 citation statements)

References 11 publications

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“…Segmentation of the minerals -in particular, granite-in thin sections of rocks using image processing methods have attracted considerable attention among geologists and others dealing with stones. Detecting the minerals [17], their size and shape [18], and the types and names of stones [19] is highly dependent on the accuracy of segmentation of minerals on the surface of the stone. The impact of the texture, mineralogy, and fractures of granite stones on the cutting efficiency was addressed in [20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this method, the final segmentation is carried out using the watershed algorithm. In another study [19], a computer program by the name of TSecSoft was developed. Based on the least color interference and by running image processing algorithms, the software separates minerals in different sections and, ultimately, the final mineral segmentation model is prepared through user adjustments and modifications.…”

Section: Literature Reviewmentioning

confidence: 99%

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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: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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

Moghaddam*¹,

Rajaei²,

Maraki³

2019

IJRTE

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“…The grain recognition based on the 2D images have the wide range of application such as river-bed grain size determination based on neural fuzzy network [11], segmentation of petrographic thin section images [12], the monitoring of an industrial flotation cell in an iron flotation plant [13] to the particle size distribution of ball-and gyro-milled lignite and hard coal [14]. Most of the recent publications on grains recognition are based on the two ways: edge-based methods such as boundaries between grains detected and analysed [15], the second one is region-based such as region growing segmentation [16]. First group of methods are based on simple thresholding technique of the gradient images with some post-processing such as thickening and skeleton for reliable grain detection.…”

Section: Related Workmentioning

confidence: 99%

Grain size determination and classification using adaptive image segmentation with shape information for milling quality evaluation

Budzan

Pawełczyk

2018

Diagnostyka

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In this paper, authors described methods of material granularity evaluation and a novel method for grain size determination with inline electromagnetic mill device diagnostics. The milling process quality evaluation can be carried out with vibration measurements, analysis of the milling material images or well-known screening machines. The method proposed in this paper is developed to the online examination of the milled product during the milling process using real-time digital images. In this paper, authors concentrated their work on copper ore milling process. Determination of the total number of the grain, the size of each grain, also the classification of the grains are the main goal of the developed method. In the proposed method the vision camera with lightning mounted at two assumed angles has been used. The detection of the grains has been based on an adaptive segmentation algorithm, improved with distance transform to enhance grains detection. The information about particles shape and context is used to optimize the grain classification process in the next step. The final classification is based on the rule-based method with defined particle shape and size parameters.

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“…This method proposes the final model of separation using watershed separation algorithm. In another research, a computer program called TSecSoft was designed by YesilogluGultekin et al [18]. This program separates the minerals in sections using the least color interfere and implementing image processing algorithms and finally, the final model of mineral separation is prepared by regulation and correction of program user.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic examination of rate of impurities and available impurities on white marble surface is an approach based on machine learning algorithms; As, one of the main objectives of examining images by automatic methods is accurate and correct separation of impurity level and available streaks on stone surface. This process has a special importance and the rate of accuracy of automatic processes like detecting ores [16], size and formation of ores [17] and detection of kinds and names of stones [18] depend greatly on the extent of accuracy of results of ore separations on stone surface. In Sanchez Delgado et al [19] it has been considered the effect of texture and study of ore and also cracks in granite stones in stone cut outputs.…”

Section: Introductionmentioning

confidence: 99%

Marble Slabs Classification System Based on Image Processing (Ark Marble Mine in Birjand)

Moghaddam

Rajaei

Moghaddam³

2018

CivileJournal

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Marble is one of the semi-precious stones that has been used in decorating building façade and making decorative things. This stone is present in the nature in the form of rock or layered stone. Examining the kind of stone, extent of impurity and different streaks in white marble is a widely confronted subject by those who are involved in this industry. Obtaining the extent of impurity of white marble using methods of detecting and analyzing material is expensive and timeconsuming. In this research carried out on while marbles of Arc Mine in Birjand, it has been attempted to present very fast method using Image Processing Techniques so that while preserving identity and appearance of stone and without any damage to it, we compute the impurity level and different streaks on white marble surface. The proposed method includes two stages; in the first stage applying image processing functions, it is attempted to segment the present impurities and streaks on marble surface from the stone background and in the second stage, the area of these impurities and streaks is computed. Results obtained in this paper (97.8%) in comparison with other researches and experimental methods indicate acceptability of this algorithm.

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A computer program (TSecSoft) to determine mineral percentages using photographs obtained from thin sections

Cited by 40 publications

References 11 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

Grain size determination and classification using adaptive image segmentation with shape information for milling quality evaluation

Marble Slabs Classification System Based on Image Processing (Ark Marble Mine in Birjand)

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