Learning How to Match Fresco Fragments

dc.contributor.authorFunkhouser, T.en_US
dc.contributor.authorShin, H.en_US
dc.contributor.authorToler-Franklin, C.en_US
dc.contributor.authorCastañeda, A. Garcíaen_US
dc.contributor.authorBrown, B.en_US
dc.contributor.authorDobkin, D.en_US
dc.contributor.authorRusinkiewicz, S.en_US
dc.contributor.authorWeyrich, T.en_US
dc.contributor.editorA. Day and R. Mantiuk and E. Reinhard and R. Scopignoen_US
dc.date.accessioned2014-02-06T15:10:40Z
dc.date.available2014-02-06T15:10:40Z
dc.date.issued2011en_US
dc.description.abstractOne of the main problems faced during reconstruction of fractured archaeological artifacts is sorting through a large number of candidate matches between fragments to find the relatively few that are correct. Previous computer methods for this task provided scoring functions based on a variety of properties of potential matches, including color and geometric compatibility across fracture surfaces. However, they usually consider only one or at most a few properties at once, and therefore provide match predictions with very low precision. In this paper, we investigate a machine learning approach that computes the probability that a match is correct based on the combination of many features. We explore this machine learning approach for ranking matches in three different sets of fresco fragments, finding that classifiers based on many match properties can be significantly more effective at ranking proposed matches than scores based on any single property alone. Our results suggest that it is possible to train a classifier on match properties in one data set and then use it to rank predicted matches in another data set effectively. We believe that this approach could be helpful in a variety of cultural heritage reconstruction systems.en_US
dc.description.seriesinformationEurographics 2011 - Areas Papersen_US
dc.identifier.issn1017-4656en_US
dc.identifier.urihttps://doi.org/10.2312/EG2011/areas/017-024en_US
dc.publisherThe Eurographics Associationen_US
dc.titleLearning How to Match Fresco Fragmentsen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
017-024.pdf
Size:
550.96 KB
Format:
Adobe Portable Document Format
Collections