GCH 2019 - Eurographics Workshop on Graphics and Cultural Heritage

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https://diglib.eg.org/handle/10.2312/2632818

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Browsing GCH 2019 - Eurographics Workshop on Graphics and Cultural Heritage by Subject "Information systems"

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    Augmented Reality (AR) Maps for Experiencing Creative Narratives of Cultural Heritage
    (The Eurographics Association, 2019) Echavarria, Karina Rodriguez; Dibble, Laurie; Bracco, Aurelie; Silverton, Edward; Dixon, Sophie; Rizvic, Selma and Rodriguez Echavarria, Karina
    This research investigates how communities can meaningfully connect with Cultural Heritage through creative and digital experiences. It also explores how entry barriers can be lowered for a wider set of audiences to increase their participation in such experiences. For this, the research investigates the use of creative and narrative-based approaches, given the potential for stories to illuminate different viewpoints and interpretations of Cultural Heritage. The paper's main technical contribution is a novel approach for re-telling communities' narratives linked to people, objects, sites and events in the urban landscape as told by the community. The research proposed the novel concept of Augmented Reality (AR) Maps, which are physical maps with augmented digital narratives and delivered through Immersive Web technology. This concept is proposed as a means to document and disseminate the narratives in a way which can enhance the public understanding and appreciation of objects and sites in their communities. The approach has been tested with 32 children in local primary school in the city of Brighton and Hove (UK) in order to understand its suitability for community engagement. The significance of the research is that it demonstrates the potential of both creative and digital approaches for enabling meaningful engagement with the Cultural Heritage, while improving the well-being of the participants as well as their sense of community and place.
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    An Automatic Approach for the Classification of Ancient Clay Statuettes Based on Heads Features Recognition
    (The Eurographics Association, 2019) Scalas, Andreas; Vassallo, Valentina; Mortara, Michela; Spagnuolo, Michela; Hermon, Sorin; Rizvic, Selma and Rodriguez Echavarria, Karina
    In recent years, quantitative approaches based on mathematical theories and ICT tools, known under the terms of digital, computational, and virtual archaeology, are more and more involved in the traditional archaeological research. In this paper, we apply shape analysis techniques to 3D digital replicas of archaeological findings to support their interpretation. In particular, our study focuses on a collection of small terracotta figurines from the ancient sanctuary of Ayia Irini, Cyprus, and it aims at re-analysing the material utilising a quantitative approach. We experiment state of the art techniques (meshSIFT and DBSCAN) to cluster statuettes according to the similarity of their heads, to investigate their production process.
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    Video Shot Analysis for Digital Curation and Preservation of Historical Films
    (The Eurographics Association, 2019) Helm, Daniel; kampel, martin; Rizvic, Selma and Rodriguez Echavarria, Karina
    In automatic video analysis and film preservation, Shot Boundary Detection (SBD) and Shot Type Classification (STC) are fundamental pre-processing steps. While previous research focuses on detecting and classifying shots in different video genres such as sports movies, documentaries or news clips only few studies investigate on SBD and STC in historical footage. In order to promote research on automatic video analysis the project Visual History of the Holocaust (VHH) has been started in January 2019. The main aim of this paper is to present first results on the fundamental topics SBD and STC in the context of the project VHH. Therefore, a deep learning-based SBD approach is implemented to detect Abrupt Transitions (ATs). Furthermore, a CNN-based algorithm is analyzed and optimized in order to classify shots into the four categories: Extreme-Long-Shot (ELS), Long-Shot (LS), Medium-Shot (MS) and Close-Up (CU). Finally, both algorithms are evaluated on a self-generated historical dataset related to the National Socialism and the Holocaust. The outcome of this paper demonstrates a first quantitative evaluation of the SBD approach and displays a F1;Score of 0.866 without the need of any re-training or optimization. Moreover, the proposed STC algorithm reaches an accuracy of 0.71 on classifying shots. This paper contributes a significant base for future research on automatic shot analysis related to the project VHH.