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Automatic Reconstruction of Glass Relics Using Manifold Learning

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dc.contributor.advisor Asmar, Daniel Kawtharani, Rabab 2024-02-12T08:50:09Z 2024-02-12T08:50:09Z 2024-02-12 2024-02-07
dc.description.abstract The risk imposed by manual reassembly on valuable broken relics necessitates automating this process by leveraging computer vision for 3D data acquisition, and data science to extract features of interest from high dimensional data. This thesis proposes a solution for the automatic reassembly of broken glass relics. The solution first relies on digitizing the broken shards. After that, contours of shards are extracted and segmented. Next, the proposed system maps the segments into the space of manifolds to quantify the similarity in their local geometry and uncover pairwise matches among them. Finally, a global optimization step finds the overall solution of the reassembly problem and the shards are aligned to visualize a digitally reassembled relic. This digital solution is then used in an application that runs on a head-mounted AR device to guide users through the process of sequentially reconstructing the real relic. The focus of this thesis is on the reconstruction part of the problem. The proposed system is discussed and verified over a dataset of broken glassware. Experiments on ten manually broken glass relics validate the success of the proposed approach by estimating the correct position of each shard in the reassembled relic. Moreover, the performance of the proposed system was tested for two extreme scenarios: missing shards and intruder shards. The system showed robust performance in finding matches among shards, however, the alignment of shards around missing pieces was effected.
dc.language.iso en_US
dc.subject Digital Reconstruction
dc.subject Computer Vision
dc.subject Digital Archeology
dc.subject Glass Reassembly
dc.subject Manifold Learning
dc.title Automatic Reconstruction of Glass Relics Using Manifold Learning
dc.type Thesis
dc.contributor.department Mechanical Engineering
dc.contributor.faculty Maroun Semaan Faculty of Engineering and Architecture
dc.contributor.commembers Elhajj, Imad
dc.contributor.commembers Mustapha, Samir
dc.contributor.commembers Panayot, Nadine ME
dc.contributor.AUBidnumber 202222252

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