Automatic mineral classification in the macroscopic scale

Marschallinger, Robert

doi:10.1016/s0098-3004(96)00074-x

Cited by 48 publications

(15 citation statements)

References 6 publications

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“…However, using static acquisition conditions for all pictures is a common practice, which yields the best results in classifying procedures. Examples of the automatic classification of rocks, conducted in the megascopic scale with static laboratory lighting, is presented by Bianconi et al (2012) and Marshallinger (1997). In the case of the microscopic scale, photographs of thin sections are taken under static lighting conditions and the same setup of polarizers is preserved for every sample (see examples in Marmo 2005 andMłynarczuk et al 2013).…”

Section: Discussionmentioning

confidence: 99%

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Application of pattern recognition methods to automatic identification of microscopic images of rocks registered under different polarization and lighting conditions

Ślipek¹,

Młynarczuk²

2013

geol

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Abstract:The paper presents the results of the automatic classification of rock images, taken under an optical microscope under different lighting conditions and with different polarization angles. The classification was conducted with the use of four pattern recognition methods: nearest neighbor, k-nearest neighbors, nearest mode, and optimal spherical neighborhoods on thin sections of five selected rocks. During research the CIELAB color space and the 9D feature space were used. The results indicate that changing both lighting conditions and polarization angles results in worsening the classification outcome, although not substantially. Duduring the automatic classification of rocks photographed under different lighting and polarization conditions, the highest number of correctly classified rocks (97%) is given by the nearest neighbor method. The results show that the automatic classification of rocks is possible within a pre-defined group of rocks. The results also indicate the optimal spherical neighborhoods method to be the safest method out of those tested, which means that it returns the lowest number of incorrect classifications.

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Section: Discussionmentioning

confidence: 99%

“…Marshallinger (1997) took macroscopic pictures of polished rock samples using a flat scanner. Next, the pictures were analyzed using special algorithms, which led to a 90% correct identification.…”

Section: Usage Of Pattern Recognition In Various Fieldsmentioning

confidence: 99%

Application of pattern recognition methods to automatic identification of microscopic images of rocks registered under different polarization and lighting conditions

Ślipek¹,

Młynarczuk²

2013

geol

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“…Unfortunately this procedure is a major obstacle for an image processing system, as the computer has to track the behaviour of a point within a grain in colour space, as well as the motion of that point as the thin section is rotated. Hence automated mineral identification systems (Launeau et al 1994;Marschallinger 1997) are based on scanned images and use the natural colour of the mineral. The rotating-polarizer microscope stage (Fueten 1997) was designed specifically as an addition to the standard petrographic microscope, to overcome some of its inherent problems.…”

Section: Mineral Identificationmentioning

confidence: 99%

Automatic mineral identification using genetic programming

Ross

Fueten

Yashkir

2001

Machine Vision and Applications

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Abstract. Automatic mineral identification using evolutionary computation technology is discussed. Thin sections of mineral samples are photographed digitally using a computer-controlled rotating polarizer stage on a petrographic microscope. A suite of image processing functions is applied to the images. Filtered image data for identified mineral grains is then selected for use as training data for a genetic programming system, which automatically synthesizes computer programs that identify these grains. The evolved programs use a decision-tree structure that compares the mineral image values with one other, resulting in a thresholding analysis of the multi-dimensional colour and textural space of the mineral images.

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“…Previously, some automatic CSD determination systems operated on reflected-light images of rock samples produced by digital scanning of polished surfaces [14,17] and used training-based colour/pattern classification techniques. In a similar manner, our system uses an 'expected appearance' model of the target mineral.…”

Section: Introductionmentioning

confidence: 99%

An image analysis method to determine crystal size distributions of olivine in kimberlite

et al. 2008

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Crystal size distributions (CSDs) are a standard method of describing populations of crystals within magmatic rocks. Olivine is the dominant phase in kimberlite (∼40-50% by volume) and features a diverse range of sizes, shapes and origins. CSDs of olivine provide a logical means of semi-quantitatively characterising kimberlite. The CSDs can then be used to distinguish or correlate between kimberlite bodies or to investigate processes related to ascent, emplacement and eruption. In this paper, we present an automatic image analysis technique that provides efficient quantification of olivine CSDs within digital images of polished slabs of kimberlite. This technique relies on a combination of algorithms for detecting regions of interest (ROI) and for segmentation of ROIs in order to identify individual olivine crystals that are used for size distribution datasets. The detection process identifies regions expected to be olivine using a modelbased colour detection technique using Mahalanobis distance combined with texture analysis based on local standard deviation and greyscale foreground enhancement techniques. The segmentation process separates adjacent domains to identify individual crystals using an iterative marker-based watershed algorithm to separate adjoined structures of varying sizes. We demonstrate the utility of automatic image analysis by comparing CSDs for olivine derived from this method versus results from manual digitisation of olivine grains. The automatic detection system correctly identified ∼86% of the manually detected olivine domains; ∼88% of the automatically detected regions correctly correlate to manually defined olivine grains. Discrepancies between the two methods are mostly the result of oversimplification of crystal margins (i.e. rounding) by manual tracing whereas automatic boundary recognition shows clear advantages in identifying irregularities in crystal edges. Closer examination of the results shows that both methods suffer from under-representation of smaller crystals due to: (1) human subjectivity and error in manual tracing and (2) noise removal processes in automatic detection. Automatic detection of olivine grains is much more efficient than conventional manual tracing; manual detection requires ∼6 h per sample versus ∼1 min for automatic analysis of the same sample.

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Automatic mineral classification in the macroscopic scale

Cited by 48 publications

References 6 publications

Application of pattern recognition methods to automatic identification of microscopic images of rocks registered under different polarization and lighting conditions

Application of pattern recognition methods to automatic identification of microscopic images of rocks registered under different polarization and lighting conditions

Automatic mineral identification using genetic programming

An image analysis method to determine crystal size distributions of olivine in kimberlite

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