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    Chapter Automation of the Expertise of the Roman Mosaic Arts in Constanta: Analytical and Statistical Models for a Fuzzy Inference-Based System

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    Author(s)
    Ţurcanu-Caruțiu, Daniela
    Ioniță, Silviu
    Language
    English
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    Abstract
    The biggest problem faced by the specialists in the field of cultural heritage is the identification of the original elements for their separation from the large mass of the mosaic components that come from completions of the different restoration works. This chapter deals with analytical models for statistical evaluation of the morphological and chromatic characteristics that represent suitable metrics for making decisions in the field of cultural heritage. A classifier model based on fuzzy logical inference, which integrates discrete and statistical characteristics of the mosaic components, is presented. The classification will be done in a space of conventional measures (metrics) for identifying and separating the mosaic components. The exemplification of the method is made on the Roman Mosaic of Constanta, a historical monument that is currently in an advanced stage of deterioration and which requires urgent restoration-conservation interventions. This artifact dates from the third or fourth century, (possibly under the emperor Constantine the Great, 306–337); it is a pavement that has decorative elements specific to this marine area, part of a large construction that took place, in antiquity on three terraces, located on the Black Sea on the docks of the old Port Tomis.
    URI
    https://library.oapen.org/handle/20.500.12657/49341
    Keywords
    automatic reasoning, expertise, mosaic artifacts, artificial intelligence
    DOI
    10.5772/intechopen.92679
    Publisher
    InTechOpen
    Publisher website
    https://www.intechopen.com/
    Publication date and place
    2020
    Classification
    Social and cultural history
    Rights
    https://creativecommons.org/licenses/by/3.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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