Segmentation of Mosaic Images Based on Deformable Models Using Genetic Algorithms

Bartoli, Alberto; Fenu, Gianfranco; Medvet, Eric; Pellegrino, Felice Andrea; Timeus, Nicola

doi:10.1007/978-3-319-61949-1_25

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…The third example is a scene from the Battle of Issus that took place in 333 BC between Alexander the Great and Darius III of Persia. The mosaic was found in the House of the Faun in Pompeii and is now part of the collection of the National Archaeological Museum in Naples 8 . A significant part of the scene around the figure of Alexander the Great is missing.…”

Section: Alexander Mosaicmentioning

confidence: 99%

“…More recently, the use of Artificial Intelligence (AI) has been proposed for different tasks related to cultural heritage, for example to manage cultural data and aid visitors to interpret museums [6] or to classify elements in models of historical buildings using sophisticated decision tree algorithms [7]. In the case of mosaics, genetic algorithms [8] and deep learning [9] have been used to segment mosaics; as AI progresses, it will find new applications in the preservation of cultural heritage.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Can Artificial Intelligence Reconstruct Ancient Mosaics?

Moral-Andrés¹,

Gómez²,

Reviriego³

et al. 2022

Preprint

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A large number of ancient mosaics have not reached us because they have been destroyed by erosion, earthquakes, looting or even used as materials in newer construction. To make things worse, among the small fraction of mosaics that we have been able to recover, many are damaged or incomplete.Therefore, restoration and reconstruction of mosaics play a fundamental role to preserve cultural heritage and to understand the role of mosaics in ancient cultures. This reconstruction has traditionally been done manually and more recently using computer graphics programs but always by humans. In the last years, Artificial Intelligence (AI) has made impressive progress in the generation of images from text descriptions and reference images. State of the art AI tools such as DALL-E2 can generate high quality images from text prompts and can take a reference image to guide the process. In august 2022, DALL-E2 launched a new feature called outpainting that takes as input an incomplete image and a text prompt and then generates a complete image filling the missing parts. In this paper,

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Section: Alexander Mosaicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can Artificial Intelligence Reconstruct Ancient Mosaics?

Moral-Andrés¹,

Gómez²,

Reviriego³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The work presented in Bartoli, Fenu, Medvet, Pellegrino, & Timeus (2016) has the same purpose where, however, deformable models are used to overlap the mosaic and adapt to the actual shape of the tesserae. To optimize the deformable forms, they use genetic algorithms.…”

Section: Related Workmentioning

confidence: 99%

Mo.Se.: Mosaic image segmentation based on deep cascading learning

Felicetti

Paolanti

Zingaretti

et al. 2021

Virtual archaeol. rev.

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p class="VARAbstract">Mosaic is an ancient type of art used to create decorative images or patterns combining small components. A digital version of a mosaic can be useful for archaeologists, scholars and restorers who are interested in studying, comparing and preserving mosaics. Nowadays, archaeologists base their studies mainly on manual operation and visual observation that, although still fundamental, should be supported by an automatized procedure of information extraction. In this context, this research explains improvements which can change the manual and time-consuming procedure of mosaic tesserae drawing. More specifically, this paper analyses the advantages of using Mo.Se. (Mosaic Segmentation), an algorithm that exploits deep learning and image segmentation techniques; the methodology combines U-Net 3 Network with the Watershed algorithm. The final purpose is to define a workflow which establishes the steps to perform a robust segmentation and obtain a digital (vector) representation of a mosaic. The detailed approach is presented, and theoretical justifications are provided, building various connections with other models, thus making the workflow both theoretically valuable and practically scalable for medium or large datasets. The automatic segmentation process was tested with the high-resolution orthoimage of an ancient mosaic by following a close-range photogrammetry procedure. Our approach has been tested in the pavement of St. Stephen's Church in Umm ar-Rasas, a Jordan archaeological site, located 30 km southeast of the city of Madaba (Jordan). Experimental results show that this generalized framework yields good performances, obtaining higher accuracy compared with other state-of-the-art approaches. Mo.Se. has been validated using publicly available datasets as a benchmark, demonstrating that the combination of learning-based methods with procedural ones enhances segmentation performance in terms of overall accuracy, which is almost 10% higher. This study’s ambitious aim is to provide archaeologists with a tool which accelerates their work of automatically extracting ancient geometric mosaics.</p><p><strong>Highlights:</strong></p><ul><li><p>A Mo.Se. (Mosaic Segmentation) algorithm is described with the purpose to perform robust image segmentation to automatically detect tesserae in ancient mosaics.</p></li><li><p>This research aims to overcome manual and time-consuming procedure of tesserae segmentation by proposing an approach that uses deep learning and image processing techniques, obtaining a digital replica of a mosaic.</p></li><li><p>Extensive experiments show that the proposed framework outperforms state-of-the-art methods with higher accuracy, even compared with publicly available datasets.</p></li></ul></div></div></div>

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“…Additionally, they propose a method to automatically get the main directional guidelines of mosaics by estimating tesserae orientation. In (Bartoli et al, 2017), for the same purpose which is tesserae detection, the authors presented a completely unsupervised approach. It consists of a deformable model to overlap the mosaic and adapt to the actual shape of each tessera, using Genetic Algorithms which evolves a fixed-size set of candidate segmentation according to a multi-objective optimization algorithm.…”

Section: Related Workmentioning

confidence: 99%

Tesserae3d: A Benchmark for Tesserae Semantic Segmentation in 3d Point Clouds

Kharroubi

Wersch

Billen

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 3D point cloud of mosaic tesserae is used by heritage researchers, restorers and archaeologists for digital investigations. Information extraction, pattern analysis and semantic assignment are necessary to complement the geometric information. Automated processes that can speed up the task are highly sought after, especially new supervised approaches. However, the availability of labelled data necessary for training supervised learning models is a significant constraint. This paper introduces Tesserae3D, a 3D point cloud benchmark dataset for training and evaluating machine learning models, applied to mosaic tesserae segmentation. It is a publicly available, very high density and coloured dataset, accompanied by a standard multi-class semantic segmentation baseline. It consists of about 502 million points and contains 11 semantic classes covering a wide range of tesserae types. We propose a semantic segmentation baseline building on radiometric and covariance features fed to ensemble learning methods. The results delineate an achievable 89% F1-score and are made available under https://github.com/akharroubi/Tesserae3D, providing a simple interface to improve the score based on feedback from the research community.

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Segmentation of Mosaic Images Based on Deformable Models Using Genetic Algorithms

Cited by 5 publications

References 8 publications

Can Artificial Intelligence Reconstruct Ancient Mosaics?

Can Artificial Intelligence Reconstruct Ancient Mosaics?

Mo.Se.: Mosaic image segmentation based on deep cascading learning

Tesserae3d: A Benchmark for Tesserae Semantic Segmentation in 3d Point Clouds

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