An innovative abstraction technique to represent both mathematically and visually some geometric properties of the facing stones in a wall is presented. The technique has been developed within the W.A.L.(L) Project, an interdisciplinary effort to apply Machine Learning techniques to support and integrate archaeological research. More precisely the paper introduces an original way to "abstract" the complex and irregular 3D shapes of stones in a wall with suitable ellipsoids. A wall is first digitized into a unique 3D point cloud and it is successively segmented into the sub-meshes of its stones. Each stone mesh is then "summarized" by the inertial ellipsoid relative to the point cloud of its vertices. A wall is in this way turned into a "population" of ellipsoid shapes statistical properties of which may be processed with Machine Learning algorithms to identify typologies of the walls under study. The paper also reports two simple case studies to assess the effectiveness of the proposed approach.

Abstracting Stone Walls for Visualization and Analysis

Buscemi F.;Riela Marco Paolo
2021

Abstract

An innovative abstraction technique to represent both mathematically and visually some geometric properties of the facing stones in a wall is presented. The technique has been developed within the W.A.L.(L) Project, an interdisciplinary effort to apply Machine Learning techniques to support and integrate archaeological research. More precisely the paper introduces an original way to "abstract" the complex and irregular 3D shapes of stones in a wall with suitable ellipsoids. A wall is first digitized into a unique 3D point cloud and it is successively segmented into the sub-meshes of its stones. Each stone mesh is then "summarized" by the inertial ellipsoid relative to the point cloud of its vertices. A wall is in this way turned into a "population" of ellipsoid shapes statistical properties of which may be processed with Machine Learning algorithms to identify typologies of the walls under study. The paper also reports two simple case studies to assess the effectiveness of the proposed approach.
Campo DC Valore Lingua
dc.authority.anceserie LECTURE NOTES IN COMPUTER SCIENCE en
dc.authority.orgunit Istituto di Scienze del Patrimonio Culturale - ISPC en
dc.authority.people Gallo G. en
dc.authority.people Buscemi F. en
dc.authority.people Ferro M. en
dc.authority.people Figuera M. en
dc.authority.people Riela Marco Paolo en
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dc.contributor.appartenenza Istituto di linguistica computazionale "Antonio Zampolli" - ILC *
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dc.date.firstsubmission 2024/12/04 13:46:39 *
dc.date.issued 2021 -
dc.date.submission 2024/12/20 14:19:46 *
dc.description.abstracteng An innovative abstraction technique to represent both mathematically and visually some geometric properties of the facing stones in a wall is presented. The technique has been developed within the W.A.L.(L) Project, an interdisciplinary effort to apply Machine Learning techniques to support and integrate archaeological research. More precisely the paper introduces an original way to "abstract" the complex and irregular 3D shapes of stones in a wall with suitable ellipsoids. A wall is first digitized into a unique 3D point cloud and it is successively segmented into the sub-meshes of its stones. Each stone mesh is then "summarized" by the inertial ellipsoid relative to the point cloud of its vertices. A wall is in this way turned into a "population" of ellipsoid shapes statistical properties of which may be processed with Machine Learning algorithms to identify typologies of the walls under study. The paper also reports two simple case studies to assess the effectiveness of the proposed approach. -
dc.description.affiliations Università di Catania-Dip. Matematica e Informatica, Catania, Italy; CNR-ISPC, Catania, Italy; Università di Catania-Dip. Matematica e Informatica, Catania, Italy; Università di Catania-Dip. Scienze Umanistiche, Catania, Italy; Università di Catania-Dip. Matematica e Informatica, Catania, Italy -
dc.description.allpeople Gallo, G.; Buscemi, F.; Ferro, M.; Figuera, M.; Riela, PAOLO MARCO -
dc.description.allpeopleoriginal Gallo G.; Buscemi F.; Ferro M.; Figuera M.; Riela Marco Paolo en
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dc.identifier.doi 10.1007/978-3-030-68787-8_15 en
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dc.publisher.country CHE en
dc.publisher.name Springer Nature Switzerland en
dc.publisher.place Basel en
dc.relation.alleditors Del Bimbo A.; Cucchiara R.; Sclaroff S.; Farinella G.M.; Mei T.; Bertini M.; Escalante H.J., Vezzani R. en
dc.relation.conferencedate 10/01/2021, 15/01/2021 en
dc.relation.conferencename ICPR: International Conference on Pattern Recognition in Cultural Heritage en
dc.relation.conferenceplace Milan, rescheduled as Virtual event due to COVID19 en
dc.relation.firstpage 215 en
dc.relation.ispartofbook Pattern Recognition. ICPR International Workshops and Challenges en
dc.relation.lastpage 222 en
dc.relation.numberofpages 8 en
dc.relation.volume 12667 en
dc.subject.keywordseng Quantitative archaeology -
dc.subject.keywordseng Data visualization -
dc.subject.keywordseng Machine learning in Cultural Heritage -
dc.subject.keywordseng Photogrammetry applied to arcaheology -
dc.subject.singlekeyword Quantitative archaeology *
dc.subject.singlekeyword Data visualization *
dc.subject.singlekeyword Machine learning in Cultural Heritage *
dc.subject.singlekeyword Photogrammetry applied to arcaheology *
dc.title Abstracting Stone Walls for Visualization and Analysis en
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