• Login
    View Item 
    •   Eurographics DL Home
    • Eurographics Workshops and Symposia
    • EG GCH: EUROGRAPHICS Workshop on Graphics and Cultural Heritage
    • GCH 2016 - Eurographics Workshop on Graphics and Cultural Heritage
    • View Item
    •   Eurographics DL Home
    • Eurographics Workshops and Symposia
    • EG GCH: EUROGRAPHICS Workshop on Graphics and Cultural Heritage
    • GCH 2016 - Eurographics Workshop on Graphics and Cultural Heritage
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Wall Painting Reconstruction Using a Genetic Algorithm

    Thumbnail
    View/Open
    083-091.pdf (4.452Mb)
    supplementary.pdf (959.5Kb)
    Date
    2016
    Author
    Sizikova, Elena
    Funkhouser, Thomas
    Pay-Per-View via TIB Hannover:

    Try if this item/paper is available.

    Metadata
    Show full item record
    Abstract
    Global reconstruction of two-dimensional wall paintings (frescoes) from fragments is an important problem for many archaeological sites. The goal is to find the global position and rotation for each fragment so that all fragments jointly "reconstruct" the original surface (i.e., solve the puzzle). Manual fragment placement is difficult and time-consuming, especially when fragments are irregularly shaped and uncolored. Systems have been proposed to first acquire 3D surface scans of the fragments and then use computer algorithms to solve the reconstruction problem. These systems work well for small test cases and for puzzles with distinctive features, but fail for larger reconstructions of real wall paintings with eroded and missing fragments due to the complexity of the reconstruction search space. We address the search problem with an unsupervised genetic algorithm (GA): we evolve a pool of partial reconstructions that grow through recombination and selection over the course of generations. We introduce a novel algorithm for combining partial reconstructions that is robust to noise and outliers, and we provide a new selection procedure that balances fitness and diversity in the population. In experiments with a benchmark dataset our algorithm is able to achieve larger and more accurate global reconstructions than previous automatic algorithms.
    BibTeX
    @inproceedings {10.2312:gch.20161388,
    booktitle = {Eurographics Workshop on Graphics and Cultural Heritage},
    editor = {Chiara Eva Catalano and Livio De Luca},
    title = {{Wall Painting Reconstruction Using a Genetic Algorithm}},
    author = {Sizikova, Elena and Funkhouser, Thomas},
    year = {2016},
    publisher = {The Eurographics Association},
    ISSN = {2312-6124},
    ISBN = {978-3-03868-011-6},
    DOI = {10.2312/gch.20161388}
    }
    URI
    http://dx.doi.org/10.2312/gch.20161388
    https://diglib.eg.org:443/handle/10.2312/gch20161388
    Collections
    • GCH 2016 - Eurographics Workshop on Graphics and Cultural Heritage

    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA
     

     

    Browse

    All of Eurographics DLCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    BibTeX | TOC

    Create BibTeX Create Table of Contents

    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA