2026-05-17T06:30:29Zhttps://iris.cnr.it/oai/request

oai:iris.cnr.it:20.500.14243/3973112026-02-07T01:24:29Zcom_20.500.14243_46com_20.500.14243_21col_20.500.14243_47ou_ou398

Abstracting Stone Walls for Visualization and Analysis Gallo G. Buscemi F. Ferro M. Figuera M. Riela Marco Paolo Del Bimbo A. Cucchiara R. Sclaroff S. Farinella G.M. Mei T. Bertini M. Escalante H.J., Vezzani R. Gallo, G. Buscemi, F. Ferro, M. Figuera, M. Riela, PAOLO MARCO Quantitative archaeology Data visualization Machine learning in Cultural Heritage Photogrammetry applied to arcaheology 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. 2021 info:eu-repo/semantics/conferenceObject https://hdl.handle.net/20.500.14243/397311 10.1007/978-3-030-68787-8_15 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85104478601 info:eu-repo/semantics/altIdentifier/isbn/978-3-030-68787-8 eng ispartofbook:Pattern Recognition. ICPR International Workshops and Challenges ICPR: International Conference on Pattern Recognition in Cultural Heritage volume:12667 firstpage:215 lastpage:222 numberofpages:8 serie:LECTURE NOTES IN COMPUTER SCIENCE info:eu-repo/semantics/restrictedAccess alleditors:Del Bimbo A.; Cucchiara R.; Sclaroff S.; Farinella G.M.; Mei T.; Bertini M.; Escalante H.J., Vezzani R. Springer Nature Switzerland country:CHE place:Basel license:Altro tipo di licenza license uri:iris.PUB01