Automatic detection of characteristic viscosity points in mineralogical samples

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hot stage microscopy (HSM) is a suitable technique for studying the behavior of mineralogical samples (such as basalt, rocks and glasses) viscosity in relation to temperature. HSM researches observe and analyze images of samples recorded during heating. This paper presents the development of a customized software, which uses digital image processing techniques to automatically detect characteristic viscosity points (CVP) based on a series of images that depict the evolution of the sample as it melts over time. This tool was developed to help determine the temperatures corresponding to CVP together with the HSM technique.

Original languageEnglish
Title of host publicationProceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016
EditorsMary Yang, Hamid R. Arabnia, Leonidas Deligiannidis, Leonidas Deligiannidis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages671-676
Number of pages6
ISBN (Electronic)9781509055104
ISBN (Print)9781509055104
DOIs
Publication statusPublished - 17 Mar 2017

Publication series

NameProceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

Fingerprint

Viscosity
Microscopy
Microscopic examination
Temperature
Basalt
Heating
Glass
Image processing
Software
Rocks
Research
basalt

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications

Cite this

Matias, I. A. C. (2017). Automatic detection of characteristic viscosity points in mineralogical samples. In M. Yang, H. R. Arabnia, L. Deligiannidis, & L. Deligiannidis (Eds.), Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016 (pp. 671-676). [7881425] (Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI.2016.0132
Matias, I.A.C. / Automatic detection of characteristic viscosity points in mineralogical samples. Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016. editor / Mary Yang ; Hamid R. Arabnia ; Leonidas Deligiannidis ; Leonidas Deligiannidis. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 671-676 (Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016).
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title = "Automatic detection of characteristic viscosity points in mineralogical samples",
abstract = "Hot stage microscopy (HSM) is a suitable technique for studying the behavior of mineralogical samples (such as basalt, rocks and glasses) viscosity in relation to temperature. HSM researches observe and analyze images of samples recorded during heating. This paper presents the development of a customized software, which uses digital image processing techniques to automatically detect characteristic viscosity points (CVP) based on a series of images that depict the evolution of the sample as it melts over time. This tool was developed to help determine the temperatures corresponding to CVP together with the HSM technique.",
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booktitle = "Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016",
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Matias, IAC 2017, Automatic detection of characteristic viscosity points in mineralogical samples. in M Yang, HR Arabnia, L Deligiannidis & L Deligiannidis (eds), Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016., 7881425, Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016, Institute of Electrical and Electronics Engineers Inc., pp. 671-676. https://doi.org/10.1109/CSCI.2016.0132

Automatic detection of characteristic viscosity points in mineralogical samples. / Matias, I.A.C.

Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016. ed. / Mary Yang; Hamid R. Arabnia; Leonidas Deligiannidis; Leonidas Deligiannidis. Institute of Electrical and Electronics Engineers Inc., 2017. p. 671-676 7881425 (Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Matias IAC. Automatic detection of characteristic viscosity points in mineralogical samples. In Yang M, Arabnia HR, Deligiannidis L, Deligiannidis L, editors, Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 671-676. 7881425. (Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016). https://doi.org/10.1109/CSCI.2016.0132