Article

A hybrid human-computer approach for recovering incomplete cultural heritage pieces

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Abstract

The automatic reconstruction of archeological pieces through the integration of a set of unknown segments is a highly complex problem which is still being researched. When only a few segments of the original piece are available, solutions exclusively based on computational algorithms are inefficient when attempting to create a credible whole restoration. Incomplete 3D puzzles must consequently be tackled by considering hybrid human/computer strategies. This paper presents a reconstruction approach in which the knowledge of human experts and computational solutions coexist together. Hypotheses, models and integration solutions originating from both humans and computers are thus continuously updated until an agreement is reached. This semi-automatic restoration approach has been tested on a set of ancient fractured pieces belonging to the remains of Roman sculptures at the well known Mérida site (Spain), and promising results have been obtained. The successful results and applicability of this method have led us to believe that computational solutions should evolve towards hybrid human–computer strategies.

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... A puzzle can be seen as a game but also as a complex activity that archaeologists undertake to reassemble fragments [16]. In addition, a hybrid human-computer strategy to solve the extremely complex 3D puzzle problems for archeological environments [17] are also studied recently. ...
Chapter
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... Technological advances have produced more precise systems, with a higher resolution and greater velocity, in addition to gradually reducing their cost. This has led to the use of 3D scanners in an increasing number of applications [2]- [5]. ...
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... De este modo, se emprendió la tarea de resolver puzles 3D. La principal contribución en este campo es la propuesta de restitución del grupo escultórico Eneas (Nogales 2007, con bibliografía anterior), que ha tenido un notable impacto científico (Adán et alii 2008;Adán, Salamanca y Merchán 2012; y más recientemente, En este caso, se digitalizaron tanto los fragmentos grandes como los trozos pequeños, en un total de 30, y se implementaron dentro de un software creado exprofeso. La creación de un algoritmo híbrido que conjugara las potencialidades de la visión por computador con los conocimientos proporcionados por los arqueólogos hizo posible la restitución dentro del grupo de las piezas más grandes de los personajes principales, junto con los pedazos menores que se habían supuesto pertenecientes a las mismas. ...
... For example, Y. Liu et al. (2012) tried to reconstruct virtual 3D historical scenes of Jing-Hang Grand Canal in China aged 2500 years ago. Adán et al. (2012) attempted to reconstruct the remains of Roman sculptures of Mérida site in Spain. ...
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The evolution of virtual reality (VR) has opened up the new perspectives especially for cultural heritage representations and preservations. We present the effort to digitize and preserve traditional Malaysian heritage games using VRML. We have built a VR game named Srikandi Melayu Terakhir. We discuss vast possibilities of using VR games in order to convert physical traditional heritage games into the ultimate gaming experience. We also present the result of the game usability evaluation. The output of this effort provides a basis to better understand VR games design especially for preserving cultural heritage traditional games.
... One can distinguish between those works dedicated to scanning open spaces and monuments (e.g. Arav et al., 2016;Sapirstein, 2016;Xu et al., 2017;Zhang et al., 2017;Balsa-Barreiro and Fritsch, 2018;P erez et al., 2019); and those which are focused on a smaller-scaled research: artefacts (Banterle et al., 2017;Graciano et al., 2017;Banducci et al., 2018;Zhang et al., 2018), epigraphy (for instance, Ramírez et al., 2017;Di Paola and Inzerillo, 2018;Carrero-Pazos and Espinosa-Espinosa, 2018;Andreu and Serrano, 2019), sculpture (Ad an et al., 2012;Zhang et al., 2015;Malik and Guidi, 2018;Perez et al., 2018), etc. ...
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... Up until recently this work was mostly carried out manually by heritage experts. The works of Adan et al., Gregor et al., and Papaioannou et al. focus on digitally reassembling broken artefacts (Adán et al., 2012, Gregor et al., 2014, Papaioannou et al., 2017. Brown et al. present a similar paper but focus on the reassembly of fresco paintings (Brown et al., 2012). ...
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... In order to solve this acutely complex 3D puzzle, we proposed a hybrid human-computer strategy suitable for archaeological environments [2]. Obviously, the strategy began with the digitization of the existing fragments using short range laser scanner to obtain accurate models. ...
Chapter
The current advances of Information and Communication Technologies (ICTs) have created new spaces for the recreational participation, mainly on virtual spaces, which can be considered as one of the main drivers of the cultural and creative production. This paper describes the experience in developing and testing an interactive 3D virtual environment for the Aeneas group in the Forum Adiectum. 3D models obtained from different sources can be included in this virtual world after a proper adaptation. We aim to demonstrate that this way of showing Cultural Heritage can motivate and facilitate people’s learning o our past rather than traditional media.
... Propusimos una estrategia híbrida humanoordenador para resolver el problema extremadamente complejo de rompecabezas 3D para entornos arqueológicos. La idea principal era que se usara una interacción entre una Base de Datos de Conocimientos Arqueológicos, construida por historiadores y arqueólogos, y una Base de Datos de Conocimientos de Computación, obtenida por expertos en informática, para lograr una solución confiable[2]. Obviamente, la estrategia comenzó con la digitalización de los fragmentos existentes utilizando un escáner láser de corto alcance para obtener modelos precisos. El escáner utilizado fue un Minolta Vivid 910. ...
... The authors of the present chapter also have extensive experience in the application of the techniques and procedures of Computer Vision to the investigation and conservation of Heritage. Also of note is the Restitution of Aeneas's sculptural group, belonging to the collection of the National Museum of Roman Art in Mérida, which has had notable scientific and media impact [8,9], or the digitization of the Fori Porticus and the Temple of Diana of Augusta Emerita (Fig. 1) or the Theatre of Segobriga [10]. ...
Chapter
This chapter presents a new line of research that addresses the treatment of data acquired through different sensors to capture reality in order to obtain useful information that can be applied to the generation of knowledge in the field of conservation and restoration of Cultural Heritage. This technology is expected to be used to solve impact problems in this field. The ultimate goal is the generation of work strategies to enhance the quality of the service provided to visitors and, consequently, their satisfaction. The multidisciplinary nature of the tasks to be tackled requires the joint work of specialists from such diverse areas as Computer Vision and Archaeology.
... However, successful modern presentations have to include modelling of the missing or unknown parts of the artefacts, which requires a large investigation process and new algorithms. Such problem is highly complex and recent semi-automatic solutions are based on human-computer approach strategies (Adan et al., 2012). Besides virtual reality, novel presentation can include augmented reality applications, too (Younesa et al., 2017). ...
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... A generic reassembly pipeline for virtual 3D artefacts (Papaioannou et al., 2017) has produced promising results, though still human visual inspection is incorporated in the process. Adán et al., (2012) proposed hybrid human-computer efforts to refine computer and archaeological knowledge bases with geometry, texture and feature knowledge to resource match performance. Tested on sculptural fragments this approach demonstrated good performance. ...
... Impediments to automated reassembly, aside from the practical difficulties associated with obtaining three-dimensional scan sets, include the extremely difficult search problems, the lack of surface information inclusion with object geometry and, significantly, the resolution of issues associated with large numbers of false-positive matches. More recently, a hybrid human-computer approach has been proposed to refine computer and archaeological knowledge bases with geometry, texture and feature knowledge to resource match performance [10]. Tested on sculptural fragments this approach demonstrated good performance. ...
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Cuneiform is one of the earliest known systems of writing consisting of wedge-shaped strokes forming signs impressed on clay tablets. Excavated cuneiform tablets are typically fragmented and their reconstruction is, at best, tedious but more often intractable given that fragments can be distributed within and between different collections. Digital archives relevant to cultural heritage, such as the Cuneiform Digital Palaeography Project and the Cuneiform Digital Library Initiative, now make richly annotated artefact media available to wide populations. Similarly, developments in computer-aided three-dimensional reconstruction methods offer the potential to assist in the restoration of broken artefacts. In this paper, a system for providing computer assistance for identifying and orientating matching fragment pairs is described. Metrics to grade the quality and likely significance of potential matches are also described. Finally, the results from experiments with scans of laboratory fabricated tablet fragments and genuine fragmented cuneiform tablets are reported.
... With the advent of new structured light sensors, such as the MS Kinect , the 3D segmentation area received a large attention from the computer vision and robotics community [10,11]. A series of implicit segmentation methods, entitled superquadrics aim at generating a 3D model S(b), defined by a series of parameters b, for approximating the volume of the given surface [12]. ...
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... This is specially important, if we take into account that the number of 3D models is growing rapidly, due to the fast evolution in both graphics hardware and software for 3D model acquisition and manipulation (e.g. [1][2][3][4]). ...
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We describe an efficient procedure for reassembling unknown two-dimensional objects that have been broken or torn into a large number of irregular fragments, a problem that often arises in archaeology, art restoration, forensics, and other disciplines. The procedure compares the curvature-encoded fragment outlines, at progressively increasing scales of resolution, using an incremental dynamic programming sequence-matching algorithm. The total cost gets reduced by a factor proportional to the mean number of samples per segment, which makes the method viable for problems of practical size (thousands of fragments). The performance of our method is illustrated with an artificial but realistic example
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The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of `shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces
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A major obstacle to the wider use of 3D object reconstruction and modeling is the extent of manual intervention needed to construct 3D models. Such interventions are currently massive and exist throughout every phase of a 3D reconstruction project: collection of images, image management, establishment of sensor position and image orientation, extracting the geometric detail describing an object, merging geometric, texture and semantic data. This work aims to develop a solution for automated documentation of archaeological pottery, which also leads to a more complete 3D model out of multiple fragments. Generally the 3D reconstruction of arbitrary objects from their fragments can be regarded as a 3D puzzle. In order to solve it we identified the following main tasks: 3D data acquisition, orientation of the object, classification of the object and reconstruction.
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We describe here an efficient procedure for reassembling unknown two-dimensional objects that have been broken or torn into a large number of irregular fragments-a problem that often arises in archaeology, art restoration, forensics, and other disciplines. The procedure compares the curvature-encoded fragment outlines, at progressively increasing scales of resolution, using an incremental dynamic programming sequence-matching algorithm. The total cost gets reduced by a factor proportional to the mean number of samples per segment, which makes the method viable for problems of practical size (thousands of fragments). The performance of our method is illustrated with an artificial but realistic example.
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A heuristic method has been developed for registering two sets of 3-D curves obtained by using an edge-based stereo system, or two dense 3-D maps obtained by using a correlation-based stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision, which is required for environment modeling (e.g., building a Digital Elevation Map). Objects are represented by a set of 3-D points, which are considered as the samples of a surface. No constraint is imposed on the form of the objects. The proposed algorithm is based on iteratively matching points in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subset-subset matching. A least-squares technique is used to estimate 3-D motion from the point correspondences, which reduces the average distance between points in the two sets. Both synthetic and real data have been used to test the algorithm, and the results show that it is efficient and robust, and yields an accurate motion estimate.
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We present a new technique for extracting line-type features on point-sampled geometry. Given an unstructuredpoint cloud as input, our method first applies principal component analysis on local neighborhoods toclassify points according to the likelihood that they belong to a feature. Using hysteresis thresholding, we thencompute a minimum spanning graph as an initial approximation of the feature lines. To smooth out the featureswhile maintaining a close connection to the underlying surface, we use an adaptation of active contour models.Central to our method is a multi-scale classification operator that allows feature analysis at multiplescales, using the size of the local neighborhoods as a discrete scale parameter. This significantly improves thereliability of the detection phase and makes our method more robust in the presence of noise. To illustrate theusefulness of our method, we have implemented a non-photorealistic point renderer to visualize point-sampledsurfaces as line drawings of their extracted feature curves.
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Modern scanning techniques, such as computed tomography, have begun to produce true three-dimensional imagery of internal structures. The first stage in finding structure in these images, like that for standard two-dimensional images, is to evaluate a local edge operator over the image. If an edge segment in two dimensions is modeled as an oriented unit line segment that separates unit squares (i.e., pixels) of different intensities, then a three-dimensional edge segment is an oriented unit plane that separates unit volumes (i.e., voxels) of different intensities. In this correspondence we derive an operator that finds the best oriented plane at each point in the image. This operator, which is based directly on the 3-D problem, complements other approaches that are either interactive or heuristic extensions of 2-D techniques.
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In this paper we deal with the problem of constructing geometric models from 3D data points from the surface of real objects. The model generated by our system handles a complete mesh of the object surface obtained by deformation of an initial mesh. Basic topologies, such as three or six connectivity, provide a local and poor characterization in the representation mesh. Therefore, we have introduced a new topological organization called modeling wave set (MWS) where an n-connectivity relationship is established. With the MWS, an object is simultaneously modeled in n subspaces of features, corresponding to n different viewing directions of the object. The use of MWS allows for new solutions to typical computer vision problems, such as recognition and positioning, to be found. Our paper shows this topology and the way it has been tested in detail. Partial modeling based on MWS is a promising research line along which we have started to work and to obtain satisfactory results.
This paper presents an algorithm for detecting the dynamically changing surface of an organ such as the breathing lung or the beating heart from a time sequence of three-dimensional density distributions, making essential use of the temporal information. Results produced by the algorithm as applied to reconstruction of real organs produced by computerized X-ray tomography are included.
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The problem of geometric alignment of two roughly pre-registered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular Iterative Closest Point (ICP) algorithm [IEEE Trans. Pattern Anal. Machine Intell. 14 (1992) 239] is presented, called Trimmed ICP (TrICP). The new algorithm is based on the consistent use of the Least Trimmed Squares approach in all phases of the operation. Convergence is proved and an efficient implementation is discussed. TrICP is fast, applicable to overlaps under 50%, robust to erroneous and incomplete measurements, and has easy-to-set parameters. ICP is a special case of TrICP when the overlap parameter is 100%. Results of a performance evaluation study on the SQUID database of 1100 shapes are presented. The tests compare TrICP and the Iterative Closest Reciprocal Point algorithm [Fifth International Conference on Computer Vision, 1995].
Conference Paper
Given in input a 3D digital model of a real object and a set of images from different view points, the problem considered is how to build a single texture map which integrates the color info contained in the input images, minimizing redundancy and optimizing the color attribute representation. We improve a previous solution (which stitches the images on the geometry) by introducing a new approach for a better color matching of the different adjoining texture chunks. The new solution applies cross-correlation and interpolation of the input images directly on the texture image space. Our averaging technique can remove color difference or discontinuity between adjacent sections of the 3D models which are mapped on different input images; in particular, the process is performed on the whole texture, contrary to other approaches that limit color smoothing on small transition zones.
Conference Paper
Building complete three-dimensional models often requires the registration and integration of multiple partial reconstructions. Registration is usually performed using a coarse-to-fine approach. We present a novel method for the crude registration of many partial three-dimensional reconstructions (patches). Our method combines the 3D measurements with the texture. Registration with methods that use only geometry will fail if the shape is too simple or symmetric. In such cases one has to use texture to resolve the ambiguities. But even then, knowledge of the shape greatly simplifies the task. The strategy we use is to intersect the surface with small spheres centered at feature points. The intersection line between such a sampling sphere and the surface defines an invariant region in the texture image. The surface texture inside these regions is used for matching regions between different patches.
Conference Paper
One of the lacks of the current conventional 3D sensor devices is that they are unable to carry out an efficient fusion between overlapped samples and generate complete and realistic reconstruction of an object (Figure 1). The completeness concerns both geometry and colour aspects (G+C). Consequently, obtaining whole (G+C) and precise registration of multiple views of an art piece is an unsolved and important problem which is required in museums and archaeological environments. In this paper we present a computer-human interactive system based on vision and touching technologies that allows us to obtain realistic 3D sculpture models. Following a global-to-local mixture strategy, millions of points with their respective colours, taken by a laser-scanner sensor, are integrated into a unique 3D model. After that, this model can be refined by a sculptor using a force-feedback device in which he can change geometrical, texture and colour properties. The final result is a high-quality restored virtual D model which can be exposed in museums or demanded by art researchers. An extensive experimentation is being carrying out on millennium sculptures and pieces of the Spanish National Museum of Roman Art yielding excellent results.
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This paper describes a general purpose, representation independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six-degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and experience shows that the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. For examples, a given 'model' shape and a sensed 'data' shape that represents a major portion of the model shape can be registered in minutes by testing one initial translation and a relatively small set of rotations to allow for the given level of model complexity. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model prior to shape inspection. The described method is also useful for deciding fundamental issues such as the congruence (shape equivalence) of different geometric representations as well as for estimating the motion between point sets where the correspondences are not known. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces.
Conference Paper
We describe a recursive multiresolution algorithm that reconstructs high-resolution and high-accuracy 3D images from low-resolution sparse range images or profiles. The method starts by creating a rough, partial, and potentially distorted estimate of the model of the object from an initial subset of sparse range data; then, using ICP algorithms, it recursively improves and refines the model by adding new range information. In parallel, real-time tracking of the object is performed in order to allow the laser scan to be automatically centered on the object. The end result is the creation of a high-resolution and accurate 3D model of a free-floating object, and real-time tracking of its position. Examples of the method are presented when the object and the 3D camera are moving freely with respect to each other. The system provides high accuracy hand-held laser scanning that does not require complex and costly mechanical scanning apparatus or external positioning devices.
Conference Paper
The iterative closest point (ICP) algorithm is a widely used method for aligning three-dimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless regions of the data, the algorithm converges slowly, finds the wrong pose, or even diverges, especially in the presence of noise or miscalibration in the input data. We describe a method for detecting uncertainty in pose, and we propose a point selection strategy for ICP that minimizes this uncertainty by choosing samples that constrain potentially unstable transformations.
In this paper, we propose a new strategy for detecting the joint among two potsherds. Joint detection problems were studied in jigsaw puzzle assembling. However, the shape assumptions of a piece used in the past researches cannot be applied to joint detection to reconstruct broken earthenware. To detect the joint, the most similar section among two contours must be detected by partial verification. In our strategy, each contour is segmented for partial verification without making any assumption about the shape of a potsherd, unlike previous jigsaw puzzle assembling methods. Our strategy consists of five processes: the contour segmentation process, the segment description process, the segment verification process, the candidate extraction process, and the candidate verification process. We also present experimental results of our strategy with two-dimensional images of potsherds
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This article presents new improvements to range image segmentation based on edge detection techniques. The developed approach better preserves the object's topology and shape even to noisy images. The algorithm also does not depend on rigid threshold values, thus being useful in unsupervised systems. Experiments were performed in a popular range image database and the results were compared to four other traditional range image segmentation algorithms, demonstrating the efficiency of the proposed algorithm.
Article
The Iterative Closest Point (ICP) algorithm is a widely used method for aligning three-dimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless regions of the data, the algorithm converges slowly, finds the wrong pose, or even diverges, especially in the presence of noise or miscalibration in the input data. In this paper, we describe a method for detecting uncertainty in pose, and we propose a point selection strategy for ICP that minimizes this uncertainty by choosing samples that constrain potentially unstable transformations.
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A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties.
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