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dc.contributor.authorSizikova, Elenaen_US
dc.contributor.authorFunkhouser, Thomasen_US
dc.contributor.editorChiara Eva Catalano and Livio De Lucaen_US
dc.date.accessioned2016-10-05T06:27:40Z
dc.date.available2016-10-05T06:27:40Z
dc.date.issued2016
dc.identifier.isbn978-3-03868-011-6
dc.identifier.issn2312-6124
dc.identifier.urihttp://dx.doi.org/10.2312/gch.20161388
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/gch20161388
dc.description.abstractGlobal 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.en_US
dc.publisherThe Eurographics Associationen_US
dc.subject2D Reconstruction
dc.subjectGenetic Programming
dc.subjectMachine Learning
dc.subjectStatistics
dc.subjectComputational Archaeology
dc.subjectData Mining
dc.subjectMachine Learning
dc.titleWall Painting Reconstruction Using a Genetic Algorithmen_US
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.description.sectionheadersAnalysis and Interpretation
dc.identifier.doi10.2312/gch.20161388
dc.identifier.pages83-91


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