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• W2918373546 abstract "A combined approach, involving 3D spatial datasets, noise removal prepossessing and deep learning regression approaches for the estimation of rough skeleton data, is presented in this paper. The application scenario involved data sequences from Greek traditional dances. In particular, a visualization application interface was developed allowing the user to load the C3D sequences, edit the data and remove possible noise. The interface was developed using the OpenGL language and is able to parse aby C3D format file. The interface is supported by several functionalities such as a pre-processing of the 3D point data and noise removal of 3D points that fall apart from the human skeleton. The main research innovation of this paper is the use of a deep machine learning framework through which human skeleton can be extracted. The points are selected on the use of a Convolutional Neural Network (CNN) model. Experimental results on real-life dances being captured by the Vicon motion capturing system are presented to show the great performance of the proposed scheme." @default.
• W2918373546 created "2019-03-11" @default.
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• W2918373546 date "2019-01-01" @default.
• W2918373546 modified "2023-09-24" @default.
• W2918373546 title "Skeleton Extraction of Dance Sequences from 3D Points Using Convolutional Neural Networks Based on a New Developed C3D Visualization Interface" @default.
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• W2918373546 doi "https://doi.org/10.1007/978-3-030-11935-5_26" @default.
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