Dance movement design based on computer three-dimensional auxiliary system
Vol 1, Issue 1, 2023
VIEWS - 800 (Abstract)
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Abstract
The Marching Cube method, a classical algorithm based on the voxel method, is used to perform three-dimensional computer-aided dance movements. Aiming at several problems in the MC algorithm, an optimization and improvement method is proposed, which realizes the organic combination of image segmentation and the MC algorithm. We use the segmented binary data to extract the isosurface; solve the ambiguity problem by establishing a look-up table of intersection conditions; use the method of quickly finding the boundary cube to avoid searching for a certain amount of empty cells; at the same time, use the midpoint method to calculate the triangle vertices. This paper verifies the effectiveness of the dance action recognition algorithm proposed in this paper on the dance data set DanceDB of the University of Cyprus and the folk dance data set FolkDance produced by the laboratory. The experimental results show that the algorithm in this paper can maintain a certain recognition rate for more complex dance movements, and the method in this paper can still guarantee a certain accuracy rate when the background and the target are easily confused. This also verifies the effectiveness of the motion recognition algorithm in this paper for dance motion recognition.
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References
1. Lucero R. Effects of instructional materials in multimedia computer-assisted instruction in teaching folk dance. Edu Sportivo: Indonesian Journal of Physical Education 2021; 2(1): 40–50. doi: 10.25299/es:ijope.2021.vol2(1).6364
2. Fernández-Ramírez J, Álvarez-Meza A, Pereira EM, Castellanos-Dominguez G. Video-based social behavior recognition based on kernel relevance analysis. The Visual Computer 2020; 36: 1535–1547. doi: 10.1007/s00371-019-01754-y
3. Gnouma M, Ladjailia A, Ejbali R, Zaied M. Stacked sparse autoencoder and history of binary motion image for human activity recognition. Multimedia Tools and Applications 2019; 78: 2157–2179. doi: 10.1007/s11042-018-6273-1
4. Misi G. Computer-aided dance analysis in practice—With Labanatory. Acta Ethnographica Hungarica 2015; 60(1): 121–170. doi: 10.1556/022.2015.60.1.14
5. Islamoglu OS, Deger KO. The location of computer aided drawing and hand drawing on design and presentation in the interior design education. Procedia-Social and Behavioral Sciences 2015; 182: 607–612. doi: 10.1016/j.sbspro.2015.04.792
6. Gale C. Art school as a transformative locus for risk in an age of uncertainty. Art, Design & Communication in Higher Education 2020; 19(1): 107–118. doi: 10.1386/adch_00016_1
7. Juliff T, Early J. The self-design of contemporary confessional art. Journal of Visual Art Practice 2019; 18(4): 342–358. doi: 10.1080/14702029.2019.1676994
8. Patrizi A, Pennestrì E, Valentini PP. Comparison between low-cost marker-less and high-end marker-based motion capture systems for the computer-aided assessment of working ergonomics. Ergonomics 2016; 59(1): 155–162. doi: 10.1080/00140139.2015.1057238
9. Leonard AE, Daily S B, Jörg S, Babu SV. Coding moves: Design and research of teaching computational thinking through dance choreography and virtual interactions. Journal of Research on Technology in Education 2021; 53(2): 159–177. doi: 10.1080/15391523.2020.1760754
10. Ruiters S, Sun Y, de Jong S, et al. Computer-aided design and three-dimensional printing in the manufacturing of an ocular prosthesis. British Journal of Ophthalmology 2016; 100(7): 879–881. doi: 10.1136/bjophthalmol-2016-308399
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