Adaptive Superpixel Segmentation and Pigment Identification of Colored Relics Based on Visible Spectral Image

Adaptive Superpixel Segmentation and Pigment Identification of Colored Relics Based on Visible Spectral Image | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adaptive Superpixel Segmentation and Pigment Identification of Colored Relics Based on Visible Spectral Image Shiwei Liu, Chun-ao Wei, Miaoxin Li, Xinyu Cui, Junfeng Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4734428/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Oct, 2024 Read the published version in npj Heritage Science → Version 1 posted 7 You are reading this latest preprint version Abstract This work explores the extraction of spatial distribution and chemical composition information of pigments used in colored relics through visible spectral images of the relics. An adaptive superpixel segmentation method is proposed first to extract the spatial distribution information of pigments. Quadtree decomposition is applied to generate nonuniform initial seed points based on image homogeneity. These seed points are used as initial cluster centers in an extended SLIC algorithm designed for visible spectral images, creating superpixels of varying sizes that reflect the homogeneity. Each superpixel is subsequently treated as an individual area within the colored relics, and a pigment identification method based on visible spectral reflectance is proposed to identify the pigments used in these areas. A standard reference database is constructed using samples that simulate the painting process of ancient wall paintings in the Mogao Grottoes. The geometric features, characterized by the linear combination of normalized visible spectral reflectance and its slope and curvature, are designed to represent the chemical composition of pigments. The geometric features of the superpixels are compared with those of the pigments in the database using Euclidean distance to determine the pigments used in each area of the colored relics. This work is expected to provide scientific guidance for pigment selection in the color restoration of colored relics. visible spectral image colored relics adaptive superpixel segmentation pigment identification standard reference database visible spectral reflectance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Oct, 2024 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 08 Aug, 2024 Reviews received at journal 05 Aug, 2024 Reviewers agreed at journal 22 Jul, 2024 Reviewers invited by journal 21 Jul, 2024 Editor assigned by journal 16 Jul, 2024 Submission checks completed at journal 16 Jul, 2024 First submitted to journal 13 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4734428","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337870338,"identity":"da9376fa-0533-4915-9fb8-81d3c613dab4","order_by":0,"name":"Shiwei Liu","email":"","orcid":"","institution":"Henan University of Animal Husbandry and Economy","correspondingAuthor":false,"prefix":"","firstName":"Shiwei","middleName":"","lastName":"Liu","suffix":""},{"id":337870339,"identity":"4285b7b6-1ce7-4b74-9fd3-bf0ac744c97f","order_by":1,"name":"Chun-ao Wei","email":"","orcid":"","institution":"Henan University of Animal Husbandry and 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