A new approach to particle shape quantification using the curvature plot

Tunwal, Mohit; Mulchrone, Kieran F.; Meere, Patrick

doi:10.1016/j.powtec.2020.07.045

Cited by 7 publications

(11 citation statements)

References 44 publications

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“…The study of particle shape can be traced back to the beginning of the last century. Researchers have conducted quantitative research on the shape of rock and soil particles and proposed a series of parameters to describe particle shape 19 , 20 , such as sphericity 21 , 22 , convexity 23 , aspect ratio 24 , and Wadell roundness 21 , 22 , 25 . Among them, Wadell roundness is an indicator used to describe the relative sharpness of particle corners.…”

Section: Introductionmentioning

confidence: 99%

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An improved corner dealiasing and recognition algorithm for 2D Wadell roundness computation

Chen,

Zhang,

Lin

et al. 2024

Sci Rep

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This paper optimizes the 2D Wadell roundness calculation of particles based on digital image processing methods. An algorithm for grouping corner key points is proposed to distinguish each independent corner. Additionally, the cyclic midpoint filtering method is introduced for corner dealiasing, aiming to mitigate aliasing issues effectively. The relationships between the number of corner pixels (m), the central angle of the corner (α) and the parameter of the dealiasing degree (n) are established. The Krumbein chart and a sandstone thin section image were used as examples to calculate the 2D Wadell roundness. A set of regular shapes is calculated, and the error of this method is discussed. When α ≥ 30°, the maximum error of Wadell roundness for regular shapes is 5.21%; when 12° ≤ α < 30°, the maximum error increases. By applying interpolation to increase the corner pixels to the minimum number (m0) within the allowable range of error, based on the α-m0 relational expression obtained in this study, the error of the corner circle can be minimized. The results indicate that as the value of m increases, the optimal range interval for n also widens. Additionally, a higher value of α leads to a lower dependence on m. The study's results can be applied to dealiasing and shape analysis of complex closed contours.

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Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to balance the smoothness and nondeformation of the particle outline during dealiasing, as this requires a compromise between insufficient and excessive 20 , 37 . When the dealiasing is insufficient, the particle outline will still have jagged edges, while excessive information will cause outline deformation.…”

Section: Introductionmentioning

confidence: 99%

An improved corner dealiasing and recognition algorithm for 2D Wadell roundness computation

Chen,

Zhang,

Lin

et al. 2024

Sci Rep

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“…The morphological (and possibly also textural) information for one grain is specified by a set of M variables (i.e., shape parameters), which might or might not be statistically independent from one another. Two-dimensional shape parameters can range from descriptors of basic geometrical characteristics (e.g., Dellino and La Volpe 1996;Cioni et al 2014;Leibrandt and Le Pennec 2015;Liu et al 2015) to more complex parameters that are the result of fractal analysis (Dellino and Liotino 2002;Maria and Carey 2002) and curvature plots (Tunwal et al 2020) or extracted from Fourier shape analysis (Barrett 1980;Suzuki et al 2015;Chávez et al 2020). In recent years, novel scanning techniques have allowed the retrieval of 3dimensional shape parameters, such as, e.g., fractal dimension (Rausch et al 2015;Vonlanthen et al 2015;Dioguardi et al 2017), aspect ratio of the best fit ellipsoid (Vonlanthen et al 2015) or 3Dsphericity (Mele et al 2011;Vonlanthen et al 2015;Dioguardi et al 2017).…”

Section: Introductionmentioning

confidence: 99%

A review of statistical tools for morphometric analysis of juvenile pyroclasts

et al. 2021

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Morphometric analyses are based on multiparametric datasets that describe quantitatively the shapes of objects. The stochastic nature of fracture-formation processes that break up magma during explosive eruptions yields mixtures of particles that have highly varied shapes. In volcanology morphometric analysis is applied to these mixtures of particles with diverse shapes for two purposes: (1) to fingerprint tephra from individual eruptions and use the fingerprints to distinguish among tephra layers and determine their extents, and (2) to reconstruct eruption processes, by linking particles formed by known fragmentation processes in experiments with particles from natural pyroclastic deposits. Here we review the most commonly adopted statistical techniques for morphometric analysis of pyroclasts. We provide sets of objects with different shapes, along with their morphometric data, in order to demonstrate and illustrate the methods. They can be used not only for addressing the processes of fragmentation during explosive eruptions, but also for the characterization of other types of solid particles with complex morphologies.

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“…These include methods based on both grainshape measurements [2,3] and visual evaluation, as proposed by Krumbein [4], Powers [5] and Cailleux [6,7]. The visual assessment of the grain shape is laborious and subjective, but it is widespread in the study of the characteristics of rocks and their genesis [8][9][10][11][12][13][14][15][16][17]. Genetic interpretation of sediments is often based on the results of sand (quartz) graincoating analyses.…”

Section: Introductionmentioning

confidence: 99%

“…Today, several available devices automatically and objectively analyse the shape of grains [14][15][16][22][23][24][25]. One of them is the Morphologi G3SE.…”

Section: Introductionmentioning

confidence: 99%

Morphometric Parameters of Krumbein Grain Shape Charts—A Critical Approach in Light of the Automatic Grain Shape Image Analysis

Szmańda

Witkowski

2021

Minerals

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Grain-shape analyses are essential in geological research because they provide the basis for genetic interpretations, including sedimentation conditions. The methods of visual evaluation used so far have been subjective, time-consuming and labour intensive. Automatic particle image analysis, including the methods used by the Morphology G3SE device, open up the possibility of mass and objective roundness analysis of mineral and organic particles. The article presents the results of measurements for the grain scale proposed by Krumbein in 1941, as this scale has been used in numerous sedimentological studies. The standard shapes were analysed using four parameters: High Sensitivity (HS) Circularity, Convexity, Solidity and Aspect Ratio. In the discussion, both the results and the grain-shape standards were critically assessed. The most important trends in the distribution of morphometric parameters of the scale are shown. On this basis, it was found that it is impossible to determine the parameter boundary values that would distinguish each class of grain roundness proposed by Krumbein. The conclusions propose criteria for the automatic differentiation of angular, subrounded and rounded grains, which could be a basis for describing the shape of mineral particles.

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A new approach to particle shape quantification using the curvature plot

Cited by 7 publications

References 44 publications

An improved corner dealiasing and recognition algorithm for 2D Wadell roundness computation

An improved corner dealiasing and recognition algorithm for 2D Wadell roundness computation

A review of statistical tools for morphometric analysis of juvenile pyroclasts

Morphometric Parameters of Krumbein Grain Shape Charts—A Critical Approach in Light of the Automatic Grain Shape Image Analysis

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