Sand grain analysis—image processing, textural algorithms and neural nets

A quantitative method for characterising and classifying quartz grain form by mathematical analysis of surface texture is presented. Scanning electron images of quartz grains were ‘frame grabbed’ and converted to a digitised grey level image. Image enhancement, segmentation and histogram equalisatio...

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Published inComputers & geosciences Vol. 24; no. 2; pp. 111 - 118
Main Authors Williams, A.T, Wiltshire, R.J, Thomas, M.C
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.03.1998
Elsevier Science
Subjects
Online AccessGet full text
ISSN0098-3004
1873-7803
DOI10.1016/S0098-3004(98)00004-1

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Abstract A quantitative method for characterising and classifying quartz grain form by mathematical analysis of surface texture is presented. Scanning electron images of quartz grains were ‘frame grabbed’ and converted to a digitised grey level image. Image enhancement, segmentation and histogram equalisation were applied to produce standardised images. Two major textural approaches were then applied. The Roberts gradient operator determined the degree of surface edgeness while calculation of spatial grey level dependence matrices allowed production of distribution maps of surface homogeneity, entropy and correlation. Textural parameters were obtained from samples of 0.5 mm quartz grains from three distinct populations: Desert quartz; Fire Island, New York, beach grains; and Brazilian crushed quartz. A comparative analysis using discriminant analysis and neural networks was undertaken to quantify the success in classifying the different populations. Both methods achieved excellent degree of quartz grain classification. However the use of neural networks provided a more robust method of analysis particularly when presented with incomplete data sets.
AbstractList A quantitative method for characterising and classifying quartz grain form by mathematical analysis of surface texture is presented. Scanning electron images of quartz grains were ‘frame grabbed’ and converted to a digitised grey level image. Image enhancement, segmentation and histogram equalisation were applied to produce standardised images. Two major textural approaches were then applied. The Roberts gradient operator determined the degree of surface edgeness while calculation of spatial grey level dependence matrices allowed production of distribution maps of surface homogeneity, entropy and correlation. Textural parameters were obtained from samples of 0.5 mm quartz grains from three distinct populations: Desert quartz; Fire Island, New York, beach grains; and Brazilian crushed quartz. A comparative analysis using discriminant analysis and neural networks was undertaken to quantify the success in classifying the different populations. Both methods achieved excellent degree of quartz grain classification. However the use of neural networks provided a more robust method of analysis particularly when presented with incomplete data sets.
Author Thomas, M.C
Wiltshire, R.J
Williams, A.T
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10.1130/0016-7606(1968)79[743:EIOSGS]2.0.CO;2
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Issue 2
Keywords Sand grain, Image analysis, Textural analysis, Statistical classification
South America
textures
Atlantic Ocean
Africa
silicates
quartz
framework silicates
North America
image analysis
grains
Language English
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– volume: 29
  start-page: 336
  year: 1985
  ident: 10.1016/S0098-3004(98)00004-1_BIB21
  article-title: Texture analysis
  publication-title: Computer Vision, Graphics and Image Processing
  doi: 10.1016/0734-189X(85)90130-6
– volume: 14
  start-page: 11
  issue: 3
  year: 1995
  ident: 10.1016/S0098-3004(98)00004-1_BIB20
  article-title: The use of Fourier descriptors in the classification of particle shape
  publication-title: Sedimentology
– ident: 10.1016/S0098-3004(98)00004-1_BIB18
– ident: 10.1016/S0098-3004(98)00004-1_BIB16
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Snippet A quantitative method for characterising and classifying quartz grain form by mathematical analysis of surface texture is presented. Scanning electron images...
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crossref
elsevier
SourceType Index Database
Enrichment Source
Publisher
StartPage 111
SubjectTerms Earth sciences
Earth, ocean, space
Exact sciences and technology
Petrology of sedimentary rocks except quaternary rocks
Sand grain, Image analysis, Textural analysis, Statistical classification
Sedimentary rocks
Title Sand grain analysis—image processing, textural algorithms and neural nets
URI https://dx.doi.org/10.1016/S0098-3004(98)00004-1
Volume 24
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