Semi-automatic geometry-driven reassembly of fractured archeological objects

Nombre pages : 6International audience3D laser scanning of broken cultural heritage content is becoming increasingly popular, resulting in large collections of detailed fractured archeological 3D objects that have to be reassembled virtually. In this paper, we present a new semi-automatic reassembly approach for pairwise matching of the fragments, that makes it possible to take into account both the archeologist's expertise, as well as the power of automatic geometry-driven matching algorithms. Our semi-automatic reassembly approach is based on a real-time interaction loop: an expert user steadily specifies approximate initial relative positions and orientations between two fragments by means of a bimanual tangible user interface. These initial poses are continuously corrected and validated in real-time by an algorithm based on the Iterative Closest Point (ICP): the potential contact surface of the two fragments is identified by efficiently pruning insignificant areas of a pair of two bounding sphere hierarchies, that is combined with a k-d tree for closest vertex queries. The locally optimal relative pose for the best match is robustly estimated by taking into account the distance of the closest vertices as well as their normals. We provide feedback to the user by a visual representation of the locally optimal best match and its associated error. Our first results on a concrete dataset show that our system is capable of assisting an expert user in real-time during the pairwise matching of downsampled 3D fragments

Interaction Tangible sur Table, définitions et modèles

International audienceIn recent years, tangible user interfaces, which imply interactions performed with one or several objects, gain more and more interest in research in Human-Computer Interaction (HCI). The tangible object represents a subject or an action. It acts on the system, as an action in classical user interfaces (e.g,. GUI). Interaction on a table, which is a common furniture in everyday life and used in multiple activities (desktop, coffee table, kitchen table, etc.), opens a new way for research and development in HCI. In this article, we present definitions, models, and key issues elicited from the literature that enable understanding and reasoning about the couple within an interactive system. Then, we propose a framework that allows to characterize applications supported by the couple in a domain-independent manner.Depuis quelques années les interfaces tangibles impliquant des interactions réalisées via un objet (ou plusieurs) prennent de plus en plus d’importance dans les recherches en interaction homme-machine. L’objet tangible représente un sujet ou une action ; l’objet agit sur le système, telle une action sur une interface « classique ». L’interaction sur table, c’est-à-dire sur un meuble présent dans la vie courante et utilisé à diverses fins (bureau, table à manger, table de salon, table bar, etc.), ouvre un champ nouveau de recherche et de développement. La mise en exergue, issue de l’état de l’art, des définitions, modèles et problématiques, permet d’abord d’appréhender le couple (table, objet tangible) au sein d’un système interactif. Puis, nous proposons un cadre qui permet de positionner des applications mettant en oeuvre le couple (table, objet tangible). Le cadre est décrit de manière à être utilisé pour positionner des applications indépendamment du domaine

Report on shape analysis and matching and on semantic matching

In GRAVITATE, two disparate specialities will come together in one working platform for the archaeologist: the fields of shape analysis, and of metadata search. These fields are relatively disjoint at the moment, and the research and development challenge of GRAVITATE is precisely to merge them for our chosen tasks. As shown in chapter 7 the small amount of literature that already attempts join 3D geometry and semantics is not related to the cultural heritage domain. Therefore, after the project is done, there should be a clear ‘before-GRAVITATE’ and ‘after-GRAVITATE’ split in how these two aspects of a cultural heritage artefact are treated.This state of the art report (SOTA) is ‘before-GRAVITATE’. Shape analysis and metadata description are described separately, as currently in the literature and we end the report with common recommendations in chapter 8 on possible or plausible cross-connections that suggest themselves. These considerations will be refined for the Roadmap for Research deliverable.Within the project, a jargon is developing in which ‘geometry’ stands for the physical properties of an artefact (not only its shape, but also its colour and material) and ‘metadata’ is used as a general shorthand for the semantic description of the provenance, location, ownership, classification, use etc. of the artefact. As we proceed in the project, we will find a need to refine those broad divisions, and find intermediate classes (such as a semantic description of certain colour patterns), but for now the terminology is convenient – not least because it highlights the interesting area where both aspects meet.On the ‘geometry’ side, the GRAVITATE partners are UVA, Technion, CNR/IMATI; on the metadata side, IT Innovation, British Museum and Cyprus Institute; the latter two of course also playing the role of internal users, and representatives of the Cultural Heritage (CH) data and target user’s group. CNR/IMATI’s experience in shape analysis and similarity will be an important bridge between the two worlds for geometry and metadata. The authorship and styles of this SOTA reflect these specialisms: the first part (chapters 3 and 4) purely by the geometry partners (mostly IMATI and UVA), the second part (chapters 5 and 6) by the metadata partners, especially IT Innovation while the joint overview on 3D geometry and semantics is mainly by IT Innovation and IMATI. The common section on Perspectives was written with the contribution of all

Analysis of 3D objects at multiple scales (application to shape matching)

Depuis quelques années, l évolution des techniques d acquisition a entraîné une généralisation de l utilisation d objets 3D très dense, représentés par des nuages de points de plusieurs millions de sommets. Au vu de la complexité de ces données, il est souvent nécessaire de les analyser pour en extraire les structures les plus pertinentes, potentiellement définies à plusieurs échelles. Parmi les nombreuses méthodes traditionnellement utilisées pour analyser des signaux numériques, l analyse dite scale-space est aujourd hui un standard pour l étude des courbes et des images. Cependant, son adaptation aux données 3D pose des problèmes d instabilité et nécessite une information de connectivité, qui n est pas directement définie dans les cas des nuages de points. Dans cette thèse, nous présentons une suite d outils mathématiques pour l analyse des objets 3D, sous le nom de Growing Least Squares (GLS). Nous proposons de représenter la géométrie décrite par un nuage de points via une primitive du second ordre ajustée par une minimisation aux moindres carrés, et cela à pour plusieurs échelles. Cette description est ensuite derivée analytiquement pour extraire de manière continue les structures les plus pertinentes à la fois en espace et en échelle. Nous montrons par plusieurs exemples et comparaisons que cette représentation et les outils associés définissent une solution efficace pour l analyse des nuages de points à plusieurs échelles. Un défi intéressant est l analyse d objets 3D acquis dans le cadre de l étude du patrimoine culturel. Dans cette thèse, nous nous étudions les données générées par l acquisition des fragments des statues entourant par le passé le Phare d Alexandrie, Septième Merveille du Monde. Plus précisément, nous nous intéressons au réassemblage d objets fracturés en peu de fragments (une dizaine), mais avec de nombreuses parties manquantes ou fortement dégradées par l action du temps. Nous proposons un formalisme pour la conception de systèmes d assemblage virtuel semi-automatiques, permettant de combiner à la fois les connaissances des archéologues et la précision des algorithmes d assemblage. Nous présentons deux systèmes basés sur cette conception, et nous montrons leur efficacité dans des cas concrets.Over the last decades, the evolution of acquisition techniques yields the generalization of detailed 3D objects, represented as huge point sets composed of millions of vertices. The complexity of the involved data often requires to analyze them for the extraction and characterization of pertinent structures, which are potentially defined at multiple scales. Amongthe wide variety of methods proposed to analyze digital signals, the scale-space analysis istoday a standard for the study of 2D curves and images. However, its adaptation to 3D dataleads to instabilities and requires connectivity information, which is not directly availablewhen dealing with point sets.In this thesis, we present a new multi-scale analysis framework that we call the GrowingLeast Squares (GLS). It consists of a robust local geometric descriptor that can be evaluatedon point sets at multiple scales using an efficient second-order fitting procedure. We proposeto analytically differentiate this descriptor to extract continuously the pertinent structuresin scale-space. We show that this representation and the associated toolbox define an effi-cient way to analyze 3D objects represented as point sets at multiple scales. To this end, we demonstrate its relevance in various application scenarios.A challenging application is the analysis of acquired 3D objects coming from the CulturalHeritage field. In this thesis, we study a real-world dataset composed of the fragments ofthe statues that were surrounding the legendary Alexandria Lighthouse. In particular, wefocus on the problem of fractured object reassembly, consisting of few fragments (up to aboutten), but with missing parts due to erosion or deterioration. We propose a semi-automaticformalism to combine both the archaeologist s knowledge and the accuracy of geometricmatching algorithms during the reassembly process. We use it to design two systems, andwe show their efficiency in concrete cases.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Analysis of 3D objects at multiple scales (application to shape matching)

Depuis quelques années, l évolution des techniques d acquisition a entraîné une généralisation de l utilisation d objets 3D très dense, représentés par des nuages de points de plusieurs millions de sommets. Au vu de la complexité de ces données, il est souvent nécessaire de les analyser pour en extraire les structures les plus pertinentes, potentiellement définies à plusieurs échelles. Parmi les nombreuses méthodes traditionnellement utilisées pour analyser des signaux numériques, l analyse dite scale-space est aujourd hui un standard pour l étude des courbes et des images. Cependant, son adaptation aux données 3D pose des problèmes d instabilité et nécessite une information de connectivité, qui n est pas directement définie dans les cas des nuages de points. Dans cette thèse, nous présentons une suite d outils mathématiques pour l analyse des objets 3D, sous le nom de Growing Least Squares (GLS). Nous proposons de représenter la géométrie décrite par un nuage de points via une primitive du second ordre ajustée par une minimisation aux moindres carrés, et cela à pour plusieurs échelles. Cette description est ensuite derivée analytiquement pour extraire de manière continue les structures les plus pertinentes à la fois en espace et en échelle. Nous montrons par plusieurs exemples et comparaisons que cette représentation et les outils associés définissent une solution efficace pour l analyse des nuages de points à plusieurs échelles. Un défi intéressant est l analyse d objets 3D acquis dans le cadre de l étude du patrimoine culturel. Dans cette thèse, nous nous étudions les données générées par l acquisition des fragments des statues entourant par le passé le Phare d Alexandrie, Septième Merveille du Monde. Plus précisément, nous nous intéressons au réassemblage d objets fracturés en peu de fragments (une dizaine), mais avec de nombreuses parties manquantes ou fortement dégradées par l action du temps. Nous proposons un formalisme pour la conception de systèmes d assemblage virtuel semi-automatiques, permettant de combiner à la fois les connaissances des archéologues et la précision des algorithmes d assemblage. Nous présentons deux systèmes basés sur cette conception, et nous montrons leur efficacité dans des cas concrets.Over the last decades, the evolution of acquisition techniques yields the generalization of detailed 3D objects, represented as huge point sets composed of millions of vertices. The complexity of the involved data often requires to analyze them for the extraction and characterization of pertinent structures, which are potentially defined at multiple scales. Amongthe wide variety of methods proposed to analyze digital signals, the scale-space analysis istoday a standard for the study of 2D curves and images. However, its adaptation to 3D dataleads to instabilities and requires connectivity information, which is not directly availablewhen dealing with point sets.In this thesis, we present a new multi-scale analysis framework that we call the GrowingLeast Squares (GLS). It consists of a robust local geometric descriptor that can be evaluatedon point sets at multiple scales using an efficient second-order fitting procedure. We proposeto analytically differentiate this descriptor to extract continuously the pertinent structuresin scale-space. We show that this representation and the associated toolbox define an effi-cient way to analyze 3D objects represented as point sets at multiple scales. To this end, we demonstrate its relevance in various application scenarios.A challenging application is the analysis of acquired 3D objects coming from the CulturalHeritage field. In this thesis, we study a real-world dataset composed of the fragments ofthe statues that were surrounding the legendary Alexandria Lighthouse. In particular, wefocus on the problem of fractured object reassembly, consisting of few fragments (up to aboutten), but with missing parts due to erosion or deterioration. We propose a semi-automaticformalism to combine both the archaeologist s knowledge and the accuracy of geometricmatching algorithms during the reassembly process. We use it to design two systems, andwe show their efficiency in concrete cases.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Enhancing Total Hip Replacement Complications Diagnosis: A Deep Learning Approach with Clinical Knowledge Integration

The increased rate of Total Hip Replacement (THR) for relieving hip pain and improving the quality of life has been accompanied by a rise in associated post-operative complications, which are evaluated and monitored mainly through clinical assessment of the X-ray images. The current clinical practice depends on the manual identification of important regions and the analysis of different features in arthroplasty X-ray images which can lead to subjectivity, prone to human error and delay diagnosis. Deep Learning (DL) based techniques showed outstanding outcomes across various image analysis tasks. However, the success of these networks is subjected to the availability of a very large, accurately annotated and well-balanced dataset - a constraint that is considered a main challenge for many medical image analysis tasks including THR. This thesis focuses on automating the analysis of THR X-ray images to aid in the diagnosis and treatment planning of various THR complications. THR X-ray images including post-operation images and after Peri-Prosthetic Femur Fracture (PFF) images of a wide range of implants and various positioning and orientations, are collected to this end. Different Convolutional Neural Network (CNN) architectures are explored for PFF classification to observe how these networks perform in the presence of class imbalance and a limited number of data and with complex image patterns, either using full X-ray images or Region of Interest (ROI) images. This demonstrates that typical CNN-based methods succeeded in detecting PFF with DenseNet achieving an F1 score of 95%, while exhibiting low performance in the classification of PFF types, achieving an F1 score of 54% with GoogleNet, Resnet and DenseNet. This lower performance is attributed to the increased complexity of the task and the imbalanced distribution of the classes. To this end, the incorporation of THR medical knowledge with DL model is investigated. The segmentation of the femoral implant component and the detection of important landmarks are formulated as simultaneous tasks within multi-task CNN that combines segmentation maps of implant with the regression of shape parameters derived from the Statistical Shape Model (SSM). Compared to the state-of-the-art, this integrated approach improves the estimation of the implant shape by a 6% dice score, making the segmentation realistic and allowing automatic detection of the important landmarks which can help in detecting many THR complications. For PFF diagnosis, the incorporation of the clinical process of interpreting THR X-ray images with CNN is developed. For this purpose, the process of clinical interpretation of PFF X-ray images is defined and the method is designed accordingly. Four feature extraction components are trained to construct features from distinctive regions of the X-ray image that are defined automatically. The extracted features are fused to classify the X-ray image into a specific fracture type. The developed approach improved PFF diagnosis by approximately 8% AUC score compared to state-of-the-art methods, signifying notable clinical advancement. Finally, the virtual pre-operative planning of bone fracture reduction surgery is explored which is important to reduce surgery time and minimize potential risks. The main obstacle toward the planning task is to define the matching between fragments. Therefore, 3D puzzle-solving method is formulated by introducing a new fragment representation and feature extraction method that improves the matching between fragments. The initial evaluation of the method demonstrates promising performance for the virtual reassembly of broken objects

Interaction en réalité mixte appliquée à l'archéologie sous-marine

L intérêt porté par l archéologie à la réalité virtuelle est croissant. La réalité virtuelle est devenue un outil nécessaire pour l exploration et l étude des sites archéologiques, et plus particulièrement, les sites archéologiques sous-marins qui se révèlent parfois difficile d accès. Les études actuelles proposent des solutions en réalité virtuelle ou en réalité augmentée sous forme d environnements virtuels avec une interaction virtuelle et/ou augmentée mais aucune étude n a vraiment essayé de comparer ces deux aspects de l interaction. Nous présentons dans ce mémoire trois environnements en réalité virtuelle et un environnement en réalité augmentée où nous proposons des nouvelles méthodes d interaction. Ainsi, nous évaluons leurs fonctionnalités d un point de vue archéologique, nous étudions l influence du niveau d immersion sur les performances de l interaction et nous réalisons une comparaison entre l interaction en réalité virtuelle et en réalité augmentée.The interest in archeology virtual reality is growing. Virtual reality has become a necessary tool for exploration and study of archaeological sites, and more specifically, the underwater archaeological sites that sometimes prove difficult to access. Current studies suggest solutions in virtual reality or augmented reality in the form of virtual environments with virtual interaction and/or augmented interaction but no studies have really tried to compare these two aspects of interaction. We present in this thesis three environments in virtual reality and an environment in augmented reality when we propose new methods of interaction. Thus, we evaluate their archaeological functionality, we study the influence of level of immersion on performance of the interaction and we make a comparison between interaction in virtual reality and interaction in augmented reality.EVRY-Bib. électronique (912289901) / SudocSudocFranceF