Automatic detection of characteristic viscosity points in mineralogical samples

I.A.C. Matias

doi:10.1109/CSCI.2016.0132

Automatic detection of characteristic viscosity points in mineralogical samples

I.A.C. Matias

Departamento de Computación e Ingeniería Industrial

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016
Editors	Mary Yang, Hamid R. Arabnia, Leonidas Deligiannidis, Leonidas Deligiannidis
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	671-676
Number of pages	6
ISBN (Electronic)	9781509055104
ISBN (Print)	9781509055104
DOIs	https://doi.org/10.1109/CSCI.2016.0132
Publication status	Published - 17 Mar 2017

Publication series

Name	Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1109/CSCI.2016.0132

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). Article 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).

@inproceedings{3e6d18250ec3454aa729f754e4739870,

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.",

author = "I.A.C. Matias",

year = "2017",

month = mar,

day = "17",

doi = "10.1109/CSCI.2016.0132",

language = "English",

isbn = "9781509055104",

series = "Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "671--676",

editor = "Mary Yang and Arabnia, {Hamid R.} and Leonidas Deligiannidis and Leonidas Deligiannidis",

booktitle = "Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016",

address = "United States",

}

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 proceeding › Conference contribution

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PY - 2017/3/17

Y1 - 2017/3/17

N2 - 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.

AB - 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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M3 - Conference contribution

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T3 - Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

SP - 671

EP - 676

BT - Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

A2 - Yang, Mary

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A2 - Deligiannidis, Leonidas

PB - Institute of Electrical and Electronics Engineers Inc.

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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). doi: 10.1109/CSCI.2016.0132

Automatic detection of characteristic viscosity points in mineralogical samples

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