A survey of classification and reconstruction methods for the 2D archaeological objects

Rasheed, Nada A.; Nordin, Jan

doi:10.1109/istmet.2015.7359018

Cited by 10 publications

(8 citation statements)

References 21 publications

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“…From the operational research perspective, jigsaw-puzzle solving is a specification of the assignment problem 35 , sharing the same logical basis and performance indices-rightful location of matching elements-as the reconstruction problem. However, most of the literature focuses on shape and pictorial content matching 36 while most reconstruction clues are rather of semantic nature 37 . The iterative modeling process allows us to follow the intermittent updates on data and predicate statements.…”

Section: Spatio-temporal Annotation and Identification Of Archeologic...mentioning

confidence: 99%

Faceting the post-disaster built heritage reconstruction process within the digital twin framework for Notre-Dame de Paris

Gros¹,

Guillem

Luca³

et al. 2023

Sci Rep

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April 15th, 2019: Notre-Dame Cathedral in Paris was burning, the spire collapsed on the nave, vaults crumbled and most of the timber roof was gone. In the post-disaster context, the authenticity and the monitoring of the archaeological remains are crucial for their potential reuse during reconstruction. This paper analyzes the collapsed transverse arch from the nave of Notre-Dame as a case study of reconstruction, using the digital twin framework. We propose four facets for the digital twin experiment—physical anastylosis, reverse engineering, spatio-temporal tracking of assets, and operational research—that are described in detail, while being assembled to support a hybrid reconstruction hypothesis. The digital twin can realize the parallel unfolding of physical-native and digital-native processes, while acquiring and storing heterogeneous information as semantically structured data. The results demonstrate that the proposed modeling method facilitates the formalization and validation of the reconstruction problem and increases solutions performances. As result, we present a digital twin framework application ranging from acquisition to data processing that informs a successful hybrid reconstruction hypothesis.

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Section: Spatio-temporal Annotation and Identification Of Archeologic...mentioning

confidence: 99%

Faceting the post-disaster built heritage reconstruction process within the digital twin framework for Notre-Dame de Paris

Gros¹,

Guillem

Luca³

et al. 2023

Sci Rep

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“…The case of automatic reassembly is extensively studied for the restoration of cultural sites and objects, as Rasheed and Nordin highlight in their surveys [18,17]. Each work they cited focus on one, two or sometimes even several issues related to archaeological puzzles (such as a large number of pieces, missing fragments, unknown orientation, erosion, fading colors).…”

Section: Archaeological Puzzlesmentioning

confidence: 99%

Deepzzle: Solving Visual Jigsaw Puzzles With Deep Learning and Shortest Path Optimization

Paumard

Picard²,

Tabia

2020

IEEE Trans. on Image Process.

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We tackle the image reassembly problem with wide space between the fragments, in such a way that the patterns and colors continuity is mostly unusable. The spacing emulates the erosion of which the archaeological fragments suffer. We crop-square the fragments borders to compel our algorithm to learn from the content of the fragments. We also complicate the image reassembly by removing fragments and adding pieces from other sources. We use a two-step method to obtain the reassemblies: 1) a neural network predicts the positions of the fragments despite the gaps between them; 2) a graph that leads to the best reassemblies is made from these predictions. In this paper, we notably investigate the effect of branch-cut in the graph of reassemblies. We also provide a comparison with the literature, solve complex images reassemblies, explore at length the dataset, and propose a new metric that suits its specificities.

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“…The reconstruction of archaeological pieces of art leads to better understanding of our history and thus attracts numerous researchers, as Rasheed and Nordin described in their surveys [6,7]. Most publications of this field rely on the border irregularities and aim for precise alignment.…”

Section: Puzzle Solvingmentioning

confidence: 99%

Image Reassembly Combining Deep Learning and Shortest Path Problem

Paumard

Picard

Tabia

2018

Lecture Notes in Computer Science

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This paper addresses the problem of reassembling images from disjointed fragments. More specifically, given an unordered set of fragments, we aim at reassembling one or several possibly incomplete images. The main contributions of this work are: 1) several deep neural architectures to predict the relative position of image fragments that outperform the previous state of the art; 2) casting the reassembly problem into the shortest path in a graph problem for which we provide several construction algorithms depending on available information; 3) a new dataset of images taken from the Metropolitan Museum of Art (MET) dedicated to image reassembly for which we provide a clear setup and a strong baseline.

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A survey of classification and reconstruction methods for the 2D archaeological objects

Cited by 10 publications

References 21 publications

Faceting the post-disaster built heritage reconstruction process within the digital twin framework for Notre-Dame de Paris

Faceting the post-disaster built heritage reconstruction process within the digital twin framework for Notre-Dame de Paris

Deepzzle: Solving Visual Jigsaw Puzzles With Deep Learning and Shortest Path Optimization

Image Reassembly Combining Deep Learning and Shortest Path Problem

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