Browsing by Author "Thompson, Elia Moscoso"

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    Automatic Segmentation of Archaeological Fragments with Relief Patterns using Convolutional Neural Networks
    (The Eurographics Association, 2021) Thompson, Elia Moscoso; Ranieri, Andrea; Biasotti, Silvia; Hulusic, Vedad and Chalmers, Alan
    The recent commodification of high-quality 3D scanners is leading to the possibility of capturing models of archaeological finds and automatically recognizing their surface reliefs. We present our advancements in this field using Convolutional Neural Networks (CNNs) to segment and classify the region around a vertex in a robust way. The network is trained with high-resolution views of the 3D models captured at different angles. The views represent both the model with its original textures and a colorization of the patches according to the value of the Shape Index (SI) in their vertices. The SI encodes local surface variations and we exploit the colorization of the model driven by the SI to generate other view and enrich the dataset. Our method has been validated on a relief recognition benchmark on archaeological fragments proposed within the SHape REtrieval Contest (SHREC) 2018.
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    SHREC 2020 Track: River Gravel Characterization
    (The Eurographics Association, 2020) Giachetti, Andrea; Biasotti, Silvia; Moscoso Thompson, Elia; Fraccarollo, Luigi; Nguyen, Quang; Nguyen, Hai-Dang; Tran, Minh-Triet; Arvanitis, Gerasimos; Romanelis, Ioannis; Fotis, Vlasis; Moustakas, Konstantinos; Tortorici, Claudio; Werghi, Naoufel; Berretti, Stefano; Schreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.
    The quantitative analysis of the distribution of the different types of sands, gravels and cobbles shaping river beds is a very important task performed by hydrologists to derive useful information on fluvial dynamics and related processes (e.g., hydraulic resistance, sediment transport and erosion, habitat suitability. As the methods currently employed in the practice to perform this evaluation are expensive and time-consuming, the development of fast and accurate methods able to provide a reasonable estimate of the gravel distribution based on images or 3D scanning data would be extremely useful to support hydrologists in their work. To evaluate the suitability of state-of-the-art geometry processing tool to estimate the distribution from digital surface data, we created, therefore, a dataset including real captures of riverbed mockups, designed a retrieval task on it and proposed them as a challenge of the 3D Shape Retrieval Contest (SHREC) 2020. In this paper, we discuss the results obtained by the methods proposed by the groups participating in the contest and baseline methods provided by the organizers. Retrieval methods have been compared using the precision-recall curves, nearest neighbor, first tier, second tier, normalized discounted cumulated gain and average dynamic recall. Results show the feasibility of gravels characterization from captured surfaces and issues in the discrimination of mixture of gravels of different size.
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    Towards an Automatic 3D Patterns Classification: the GRAVITATE Use Case
    (The Eurographics Association, 2018) Thompson, Elia Moscoso; Biasotti, Silvia; Sorrentino, Giusi; Polig, Martina; Hermon, Sorin; Sablatnig, Robert and Wimmer, Michael
    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.