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Boundary Morphology for Hierarchical Simplification of Archaeological Fragments

H. ElNaghy, L. Dorst


Abstract: When fitting archaeological artifacts, one would like to have a representation that simplifies fragments while preserving their complementarity. In  this paper, we propose to employ the scale-spaces of mathematical morphology to hierarchically simplify potentially fitting fracture surfaces. We study the masking effect when morphological operations are applied to selected subsets of objects. Since fitting locally depends on the complementarity of fractures only, we introduce ‘Boundary Morphology’ on surfaces rather than volumes. Moreover, demonstrating the Lipschitz nature of the terracotta fractures informs our novel extrusion method to compute both closing and opening operations simultaneously.We also show that in this proposed representation the effects of abrasion and uncertainty are naturally bounded, justifying the morphological approach. This work is an extension of our contribution earlier published in the proceedings of ISMM2019 [10].

Complementarity-Preserving Fracture Morphology for Archaeological Fragments

H. ElNaghy, L. Dorst


Abstract: We propose to employ scale spaces of mathematical morphology to hierarchically simplify fracture surfaces of complementarity fitting archaeological fragments. This representation preserves complementarity and is insensitive to different kinds of abrasion affecting the exact fitting of the original fragments. We present a pipeline for morphologically simplifying fracture surfaces, based on their Lipschitz nature; its core is a new embedding of fracture surfaces to simultaneously compute both closing and opening morphological operations, using distance transforms.


Solving Archaeological Puzzles

N. Derech, A. Tal, I. Shimshon

December 2018

Abstract: Puzzle solving is a difficult problem in its own right, even when the pieces are all square and build up a natural image. But what if these ideal conditions do not hold? One such application domain is archaeology, where restoring an artifact from its fragments is highly important. From the point of view of computer vision, archaeological puzzle solving is very challenging, due to three additional difficulties: the fragments are of general shape; they are abraded, especially at the boundaries (where the strongest cues for matching should exist); and the domain of valid transformations
between the pieces is continuous. The key contribution of this paper is a fully-automatic and general algorithm that addresses puzzle solving in this intriguing domain. We show that our state-of-the-art approach manages to correctly reassemble dozens of broken artifacts and frescoes.


Shape Analysis Techniques for the Ayia Irini Case Study.

A. Scalas, V. Vassallo, M. Mortara, M. Spagnuolo, S. Hermon

Eurographics workshop on Graphics and Cultural Heritage 2018


Abstract: The typical approach for archaeological analysis is mainly qualitative and, as such, subjective. Even when some measures are
reported in the documentation of artefacts, they are often approximate or ambiguous. Conversely, the quantitative approach is
based on objective metrics to produce replicable results and, coupled with digital tools, can assist the qualitative analysis in
archaological research with no risk of damage.
In this paper, we present a geometric-quantitative approach for the analysis of archaeological finds and the preliminary results
of an ongoing joint research project of two doctoral students within the frame of the EU GRAVITATE project.


Towards an Automatic 3D Patterns Classification: the GRAVITATE Use Case

E. Moscoso Thompson, S. Biasotti, G. Sorrentino, M. Polig, S. Hermon

Eurographics Workshop on Graphics and Cultural Heritage 2018


Abstract: When cataloging archaeological fragments, decorative patterns are an indicator of the stylistic canon an object belongs to. In this paper we address a quantitative classification of the decorative pattern elements that characterize the models in the GRAVITATE use case, discussing the performance of a recent algorithm for pattern recognition over triangle meshes.


A Cultural Heritage partonomy for the documentation of 3D digital artefacts of Cypriot coroplastic art

C.E. Catalano, V. Vassallo, S. Hermon, M. Spagnuolo

CIDOC Conference 2018


Abstract: The goal of this paper is defining a Cultural Heritage Artefact Partonomy (CHAP) concerning coroplastic Cypriot art. In particular, two case studies have been considered: the terracotta statues from the port of Salamis, attributed to the Neo-Cypriote style (ca. 600-500 BC), and the small clay statuettes from the Ayia Irini sanctuary, mostly attributed to the Cypro-Archaic period (700-500 BC). Although their differences in size, style and decorations, the items of this study represent male standing bearded figures, sometimes holding animals, arms or music instruments, and provide interesting examples for the description of human figures and their attributes in ancient times. Moreover, 3D digital models have been created digitising the physical artefacts for archaeological purposes.The objective of this work is framed within the EU GRAVITATE project, which proposes an innovative approach to the study of heritage artefacts, including 3D virtual reconstruction, classification and morphological analysis, steps that are limited by the impossibility to re-unite them physically, either because they are stored in various museums or because physical refitting fails. In this perspective, a controlled vocabulary for the documentation and retrieval of 3D digital fragments and their parts has been developed and proposed here. CHAP is a SKOS vocabulary, aligned and mapped to CIDOC CRM to integrate the description of the relationships between the parts and the overall context of the two archaeological collections. Focussing on both the artefacts and their digital counterparts, CHAP refers also to the CIDOC-CRMdig extension, where possible missing components have been identified and undertaken.


Description and retrieval of geometric patterns on surface meshes using an edge-based LBP approach

E. Moscoso Thompson, S. Biasotti

Pattern Recognition, Vol. 82

October 2018

Abstract: While texture analysis is largely addressed for images, the comparison of the geometric reliefs on surfaces embedded in the 3D space is still an open challenge. Starting from the Local Binary Pattern (LBP) description originally defined for images, we introduce the edge-Local Binary Pattern (edgeLBP) as a local description able to capture the evolution of repeated, geometric patterns on surface meshes. Our extension is independent of the surface representation, indeed the edgeLBP is able to deal with surface tessellations characterized by non-uniform vertex distributions and different types of faces, such as triangles, quadrangles and, in general, convex polygons. Besides the desirable robustness properties the edgeLBP exhibits over a number of examples, we show how this description performs well for 3D pattern retrieval and compare our performances with the participants to a recent 3D pattern retrieval and classification contest.



Experimental Similarity Assessment for a Collection of Fragmented Artifacts

S. Biasotti, E. Moscoso Thompson, M. Spagnuolo

Eurographics Workshop on 3D Object Retrieval


Abstract: In the Visual Heritage domain, search engines are expected to support archaeologists and curators to address cross-correlation and searching across multiple collections. Archaeological excavations return artifacts that often are damaged with parts that are fragmented in more pieces or totally missing. The notion of similarity among fragments cannot simply base on the geometric shape but style, material, color, decorations, etc. are all important factors that concur to this concept. In this work, we discuss to which extent the existing techniques for 3D similarity matching are able to approach fragment similarity, what is missing and what is necessary to be further developed.


Edge-based LBP Description of Surfaces with Colorimetric Patterns

E. Moscoso Thompson, S. Biasotti

Eurographics Workshop on 3D Object Retrieval


Abstract: In this paper we target the problem of the retrieval of colour patterns over surfaces. We generalize to surface tessellations the well known Local Binary Pattern (LBP) descriptor for images. The key concept of the LBP is to code the variability of the colour values around each pixel. In the case of a surface tessellation we adopt rings around vertices that are obtained with a sphere-mesh intersection driven by the edges of the mesh; for this reason, we name our method edgeLBP. Experimental results are provided to show how this description performs well for pattern retrieval, also when patterns come from degraded and corrupted archaeological fragments.


Retrieval of Gray Patterns Depicted on 3D Models

E. Moscoso Thompson, C. Tortorici, N. Werghi, S. Berretti, S. Velasco-Forero, S. Biasotti

Eurographics Workshop on 3D Object Retrieval


Abstract: This paper presents the results of the SHREC'18 track: Retrieval of gray patterns depicted on 3D models. The task proposed in the contest challenges the possibility of retrieving surfaces with the same texture pattern of a given query model. This task, which can be seen as a simplified version of many real world applications, requires a characterization of the surfaces based on local features, rather then considering the surface size and/or bending. All runs submitted to this track are based on feature vectors. The retrieval performances of the runs submitted for evaluation reveal that texture pattern retrieval is a challenging issue. Indeed, a good balance between the size of the pattern and the dimension of the region around a vertex used to locally analyze the color evolution is crucial for pattern description.


Recognition of Geometric Patterns Over 3D Models

S. Biasotti, E. Moscoso Thompson, L. Barthe, S. Berretti, A. Giachetti,  T. Lejemble, N. Mellado, K.  Moustakas, I. Manolas, D. Dimou, C. Tortorici, S. Velasco-Forero, N. Werghi, M. Polig, G. Sorrentino, S. Hermon

Eurographics Workshop on 3D Object Retrieval


Abstract: This track of the SHREC 2018 originally aimed at recognizing relief patterns over a set of triangle meshes from laser scan acquisitions of archaeological fragments. This track approaches a lively and very challenging problem that remains open after the end of the track. In this report we discuss the challenges to face to successfully address geometric pattern recognition over surfaces; how the existing techniques can go further in this direction, what is currently missing and what is necessary to be further developed.


Using Mathematical Morphology to Simplify Archaeological Fracture Surfaces

H. ElNaghy, L. Dorst

Eurographics Symposium for Geometry Processing


Abstract: It is computationally expensive to fit the high-resolution 3D meshes of abraded fragments of archaeological artefacts in a collection. Therefore, simplification of fracture surfaces while preserving the fitting essentials is required to guide and structure the whole reassembly process. Features of the scale spaces from Mathematical Morphology (MM) permit a hierarchical approach to this simplification, in a contact-preserving manner, while being insensitive to missing geometry. We propose a new method to focusing MM on the fracture surfaces only, by an embedding that uses morphological duality to compute the desired opening by a closing. The morphological scale space operations on the proposed dual embedding of archaeological fracture surfaces are computed in a distance transform treatment of voxelized meshes.


Geometry Based Faceting of 3D Digitized Archaeological Fragments

H. ElNaghy, L. Dorst

The IEEE International Conference on Computer Vision (ICCV)

October  2017

Abstract: We present a robust pipeline for segmenting digital cultural heritage fragments into distinct facets, with few tunable yet archaeologically meaningful parameters. Given a terracotta broken artifact, digitally scanned in the form of irregularly sampled 3D mesh, our method first estimates the local angles of fractures by applying weighted eigenanalysis of the local neighborhoods. Using 3D fit of a quadratic polynomial, we estimate the directional derivative of the angle function along the maximum bending direction for accurate localization of the fracture lines across the mesh. Then, the salient fracture lines are detected and incidental possible gaps between them are closed in order to extract a set of closed facets. Finally, the facets are categorized into fracture and skin. The method is tested on two different datasets of the GRAVITATE project.


3D Annotation Transfer

A. Scalas, M. Mortara, M. Spagnuolo

15th EUROGRAPHICS Workshop on Graphics and Cultural Heritage

September 2017

Abstract: In the last few years, there has been an increase in digitalization efforts within the Cultural Heritage field, which boosted the interest for new strategies to improve documentation standards. While these concepts have been largely studied for most of the CH content types, 3D data still need to be fully worked out as document types. One of the most innovative methods to glue the documentation (i.e. the semantics) of the artifacts to their geometry is to exploit the technology of the semantic web and implement the semantic annotation pipeline for 3D data. Since the 3D representation of artifacts is not a standard, and in the particular case of triangular meshes there are differences of resolutions and vertices position, there is the strong need for tools which could allow for annotation persistence between representation switch. In this paper, we present the first results in the design of an automatic algorithm for annotation transfer between triangular meshes with different resolutions, provided that they represent the same artifact.


A Dashboard for the Analysis of Tangible Heritage Artefacts: a Case Study in Archaeology

C. E. Catalano, A. Repetto, M. Spagnuolo

15th EUROGRAPHICS Workshop on Graphics and Cultural Heritage

September 2017

Abstract: Digital manipulation and analysis of tangible cultural objects has the potential to bring about a revolution in the way classification, stylistic analysis, and refitting of fragments are handled in the cultural heritage area: 3D modelling, processing and analysis are now mature enough to allow handling 3D digitized objects as if they were physical, and semantic models allow for a rich documentation of many different aspects of artefacts or assets of any complexity, as well as of contextual information about them. In this perspective, the paper presents the ongoing development of a software workbench which integrates several tools that can be used, combined, and customized to provide scientists with a working environment to process and analyse digital assets. The general objective is to exemplify the potential of new platforms to work on digital models beyond the simple rendering and visualization of assets. In particular, the paper presents the design of the workbench - the Dashboard - which reflects the analysis of the requirements gathered in a specific community of archaeologists and curators: the functionalities included in the case study target mostly the ReUnification, ReAssembly and ReAssociation of fragmented or dispersed cultural assets.


Streamlining the Preparation of Scanned 3D Artifacts to Support Digital Analysis and Processing: the GRAVITATE Case Study

M. Mortara, C. Pizzi, M. Spagnuolo

15th EUROGRAPHICS Workshop on Graphics and Cultural Heritage

September 2017

Abstract: Digitally acquired 3D models of cultural assets are not always ready for further processing. Sometimes, the digital surface presents geometric or topological defects that may hinder downstream surface analysis algorithms. Furthermore, the high resolution meshes provided by acquisition might pose complexity issues to the processing afterwards. Preprocessing models can be a tedious and sometimes manual work. We present the processing needs for a set of cultural artifacts in the framework of the GRAVITATE project and describe a fully automatic procedure to fix and adaptively simplify 3D models of cultural interest.


Recognition of feature curves on 3D shapes using an algebraic approach to Hough transforms

M.L. Torrente, S. Biasotti, B. Falcidieno

Pattern Recognition, Vol. 73

August 2017

Abstract: Feature curves are largely adopted to highlight shape features, such as sharp lines, or to divide surfaces into meaningful segments, like convex or concave regions. Extracting these curves is not sufficient to convey prominent and meaningful information about a shape. We have first to separate the curves belonging to features from those caused by noise and then to select the lines, which describe non-trivial portions of a surface. The automatic detection of such features is crucial for the identification and/or annotation of relevant parts of a given shape. To do this, the Hough transform (HT) is a feature extraction technique widely used in image analysis, computer vision and digital image processing, while, for 3D shapes, the extraction of salient feature curves is still an open problem. Thanks to algebraic geometry concepts, the HT technique has been recently extended to include a vast class of algebraic curves, thus proving to be a competitive tool for yielding an explicit representation of the diverse feature lines equations. In the paper, for the first time we apply this novel extension of the HT technique to the realm of 3D shapes in order to identify and localize semantic features like patterns, decorations or anatomical details on 3D objects (both complete and fragments), even in the case of features partially damaged or incomplete. The method recognizes various features, possibly compound, and it selects the most suitable feature profiles among families of algebraic curves.


Retrieval of surfaces with similar relief patterns

S. Biasotti, E. Moscoso Thompson, M. Aono, A. Ben Hamza, B. Bustos, S. Dong, B. Du, A. Fehri, H. Li, F. A. Limberger, M. Masoumi, M. Rezaei, I. Sipiran, L. Sun, A. Tatsuma, S. Velasco Forero, R. C. Wilson, Y. Wu, J. Zhang, T. Zhao, F. Fornasa, A. Giachetti

Eurographics Workshop on 3D Object Retrieval

April 2017

Abstract: This paper presents the results of the SHREC’17 contest on retrieval of surfaces with similar relief patterns. The proposed task was created in order to verify the possibility of retrieving surface patches with a relief pattern similar to an example from a database of small surface elements. This task, related to many real world applications, requires an effective characterization of local "texture" information not depending on patch size and bending. Retrieval performances of the proposed methods reveal that the problem is not quite easy to solve and, even if some of the proposed methods demonstrate promising results, further research is surely needed to find effective relief pattern characterization techniques for practical applications.


Feature identification in archaeological fragments using families of algebraic curves

M.L. Torrente, S. Biasotti, B. Falcidieno

Eurographics Workshop on Graphics and Cultural Heritage

October 2016

Abstract: A method is proposed to identify and localize semantic features like anatomical characteristics or decorations on digital artefacts or fragments, even if the features are partially damaged or incomplete. This technique is based on a novel generalization of the Hough transform. Its major advantages are the relative robustness to noise and the recognition power also in the case of partial features. Our experiments on digital models of real artefacts show the potential of the method, which can work on both 3D meshes and point clouds.


Feature curve identification in archaeological fragments using an extension of the Hough transform

M.L. Torrente, S. Biasotti, B. Falcidieno

IMATI REPORT Series, Nr. 16-09

July 2016

Abstract: The use of computer graphics techniques in cultural heritage (CH) has led to impressive improvements in technologies related to digital acquisition and rendering of 3D CH data. Digitized artefacts are becoming widely available for access and reuse, thus increasing the need of tools able to support comparative shape analysis. As 3D artefacts are often worn, eroded and broken, these tools cannot take advantage of existing methods based on exact matching but they rather require new approaches able to identify partial features in portions of models thus leading to a double partiality of the matching problem, in terms of both features and models In this context, we propose a method based on a novel generalization of the Hough transform technique able to identify and localize semantic features like anatomical features, ornaments, or decorations on digital artefacts or fragments, even if the features are partially damaged or incomplete. The major advantages of using a method based on the Hough transform technique are the relative robustness to noise and the recognition power also in the case of partial features. Our experiments on digital models of real artefacts are encouraging and show the potential of the method, which can work on both 3D meshes and point clouds.


GRAVITATE – Geometric and Semantic Matching for Cultural Heritage Artefacts

S.C. Phillips, P.W. Walland, S. Modafferi, L. Dorst, M. Spagnuolo, C.E. Catalano, D. Oldman, A. Tal, I. Shimshoni, S. Hermon

Eurographics Workshop on Graphics and Cultural Heritage

May 2016

Abstract: The GRAVITATE project is developing techniques that bring together geometric and semantic data analysis to provide a new and more effective method of re-associating, reassembling or reunifying cultural objects that have been broken or dispersed over time. The project is driven by the needs of archaeological institutes, and the techniques are exemplified by their application to a collection of several hundred 3D-scanned fragments of large-scale terracotta statues from Salamis, Cyprus. The integration of geometrical feature extraction and matching with semantic annotation and matching into a single decision support platform will lead to more accurate reconstructions of artefacts and greater insights into history. In this paper we describe the project and its objectives, then we describe the progress made to date towards achieving those objectives: describing the datasets, requirements and analysing the state of the art. We follow this with an overview of the architecture of the integrated decision support platform and the first realisation of the user dashboard. The paper concludes with a description of the continuing work being undertaken to deliver a workable system to cultural heritage curators and researchers.


Color Restoration of Scanned Archaeological Artifacts with Repetitive Patterns

D. Gilad-Glickman, I. Shimshoni

Eurographics Workshop on Graphics and Cultural Heritage

May 2016

Abstract: Our work addresses the problem of virtually restoring archaeological artifacts. Virtual restoration is the process of creating a noise-free model of a degraded object, to visualize its original appearance. Our work focuses on restoring the coloring of the object. We considered both 2D and 3D objects, including scans of ancient texts and 3D models of decorated pottery. Our denoising method exploits typical characteristics of archaeological artifacts, such as repetitive decoration motifs and a limited palette of colors. Our classification method is based on minimization of an energy function, which includes a correspondence term, to encourage consistent labeling of similar regions. The energy function is minimized using the Graph-Cuts algorithm.


Solving Multiple Square Jigsaw Puzzles With Missing Pieces

G. Paikin, A. Tal

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

June 2015

Abstract: Jigsaw-puzzle solving is necessary in many applications, including biology, archaeology, and every-day life. In this paper we consider the square jigsaw puzzle problem, where the goal is to reconstruct the image from a set of non-overlapping, unordered, square puzzle parts. Our key contribution is a fast, fully-automatic, and general solver, which assumes no prior knowledge about the original image. It is general in the sense that it can handle puzzles of unknown size, with pieces of unknown orientation, and even puzzles with missing pieces. Moreover, it can handle all the above, given pieces from multiple puzzles. Through an extensive evaluation we show that our approach outperforms state-of-the-art methods on commonly-used datasets.