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Digitizing the Intangible: Machine Learning Applications Over Complex 3D Trajectories
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Author(s): Eftychios Protopapadakis (National Technical University of Athens, Greece), Ioannis Rallis (National Technical University of Athens, Athens, Greece), Nikolaos Bakalos (National Technical University of Athens, Athens, Greece)and Maria Kaselimi (National Technical University of Athens, Athens, Greece)
Copyright: 2020
Pages: 29
Source title:
Recent Advances in 3D Imaging, Modeling, and Reconstruction
Source Author(s)/Editor(s): Athanasios Voulodimos (University of West Attica, Athens, Greece)and Anastasios Doulamis (National Technical University of Athens, Athens, Greece)
DOI: 10.4018/978-1-5225-5294-9.ch004
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Abstract
Modelling and digitizing performing arts through motion capturing interfaces is an important aspect for the analysis, processing, and documentation of intangible cultural heritage assets. This chapter provides a holistic description regarding the dance preservation topic by describing the capture approaches, efficient preprocessing techniques, and specific approaches for knowledge generation. Presented methodologies take under consideration the existing modelling and interpretation approaches, which may involve huge amounts of information, making them difficult to process, store, and analyze.
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