Publication detail

Image analysis applied to quartz grain microtextural provenance studies

ŘÍHA, K. KŘUPKA, A. COSTA, P.

Original Title

Image analysis applied to quartz grain microtextural provenance studies

Type

journal article in Web of Science

Language

English

Original Abstract

Every quartz grain surface is sculptured during time by many different processes. During the life of a grain, sediment transport, physical and chemical processes imprint specific microtextural features on their surface. State-of-the-art methods for analysis of the surface of quartz particles in Scanning Electron Microscope images are performed non-automatically by experts and essentially are dependent on operator proficiency. Here, we propose digital image processing-based method for automated microtextural analysis of grain's surface with the goal to classify the grain into specific classes corresponding to its genesis (last provenance cycle). The method is based on original classification scheme which extracts the textural features from the digital images of single grains in training database and creates Texton Dictionary for establishing the prototype of specific texture set characterizing given genesis class. The created Texton Dictionary then can be used for classification of a grain with unknown genesis with very good performances when tested on databases of grains with known origins.

Keywords

MicrotexturesImage analysisSedimentologyQuartz grainsTexton dictionary

Authors

ŘÍHA, K.; KŘUPKA, A.; COSTA, P.

Released

9. 4. 2019

ISBN

0098-3004

Periodical

COMPUTERS & GEOSCIENCES

Number

125

State

United Kingdom of Great Britain and Northern Ireland

Pages from

98

Pages to

108

Pages count

11

URL

https://www.sciencedirect.com/science/article/pii/S0098300418305752

BibTex

@article{BUT157481,
  author="Kamil {Říha} and Aleš {Křupka} and Pedro J.M. {Costa}",
  title="Image analysis applied to quartz grain microtextural provenance studies",
  journal="COMPUTERS & GEOSCIENCES",
  year="2019",
  number="125",
  pages="98--108",
  doi="10.1016/j.cageo.2019.01.007",
  issn="0098-3004",
  url="https://www.sciencedirect.com/science/article/pii/S0098300418305752"
}