Visualizing Networks of Maya Glyphs by Clustering Subglyphs

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dc.contributor.authorBogacz, Bartoszen_US
dc.contributor.authorFeldmann, Felixen_US
dc.contributor.authorPrager, Christianen_US
dc.contributor.authorMara, Huberten_US
dc.contributor.editorSablatnig, Robert and Wimmer, Michaelen_US
dc.date.accessioned2018-11-11T10:57:30Z
dc.date.available2018-11-11T10:57:30Z
dc.date.issued2018
dc.description.abstractDeciphering the Maya writing is an ongoing process that has already started in the early 19th century. Among the reasons why Maya hieroglyphic script and language are still undeciphered are inexpertly-created drawings of Maya writing systems resulting in a large number of misinterpretations concerning the contents of these glyphs. As a consequence, the decipherment of Maya writing systems has experienced several setbacks. Modern research in the domain of cultural heritage requires a maximum amount of precision in capturing and analyzing artifacts so that scholars can work on - preferably - unmodified data as much as possible. This work presents an approach to visualize similar Maya glyphs and parts thereof and enable discovering novel connections between glyphs based on a machine learning pipeline. The algorithm is demonstrated on 3D scans from sculptured monuments, which have been filtered using a Multiscale Integral Invariant Filter (MSII) and then projected as a 2D image. Maya glyphs are segmented from 2D images using projection profiles to generate a grid of columns and rows. Then, the glyphs themselves are segmented using the random walker approach, where background and foreground is separated based on the surface curvature of the original 3D surface. The retrieved subglyphs are first clustered by their sizes into a set of common sizes. For each glyph a feature vector based on Histogram of Gradients (HOG) is computed and used for a subsequent hierarchical clustering. The resultant clusters of glyph parts are used to discover and visualize connections between glyphs using a force directed network layout.en_US
dc.description.sectionheadersVisualization and Visual Analytics for CH
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.identifier.doi10.2312/gch.20181346
dc.identifier.isbn978-3-03868-057-4
dc.identifier.issn2312-6124
dc.identifier.pages105-111
dc.identifier.urihttps://doi.org/10.2312/gch.20181346
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/gch20181346
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectGraph drawings
dc.subjectInformation visualization
dc.subjectComputing methodologies
dc.subjectObject identification
dc.subjectCluster analysis
dc.subjectApplied computing
dc.subjectOptical character recognition
dc.titleVisualizing Networks of Maya Glyphs by Clustering Subglyphsen_US
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GCH 2018 - Eurographics Workshop on Graphics and Cultural Heritage