@inproceedings{Bakalos2020,

title = {Adaptive Convolutionally Enchanced Bi-Directional Lstm Networks For Choreographic Modeling},

author = {Nikolaos Bakalos and Ioannis Rallis and Nikolaos Doulamis and Anastasios Doulamis and Athanasios Voulodimos and Eftychios Protopapadakis},

url = {https://ieeexplore.ieee.org/document/9191307/},

doi = {10.1109/ICIP40778.2020.9191307},

isbn = {978-1-7281-6395-6},

year  = {2020},

date = {2020-10-01},

booktitle = {2020 IEEE Int. Conf. Image Process.},

pages = {1826--1830},

publisher = {IEEE},

abstract = {In this paper, we present a deep learning scheme for classification of choreographic primitives from RGB images. The proposed framework combines the representational power of feature maps, extracted by Convolutional Neural Networks, with the long-term dependency modeling capabilities of Long Short-Term Memory recurrent neural networks. In addition, it uses AutoRegressive and Moving Average (ARMA) filter into the convolutionally enriched LSTM filter to face dance dynamic characteristics. Finally, an adaptive weight updating strategy is introduced for improving classification modeling performance The framework is used for the recognition of dance primitives (basic dance postures) and is experimentally validated with real-world sequences of traditional Greek folk dances.},

keywords = {CNN, Convolutional LSTM, Folkloric dances, Intangible Cultural Heritage, LSTM, Posture identification},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Doulamis2017,

title = {Transforming Intangible Folkloric Performing Arts into Tangible Choreographic Digital Objects: The Terpsichore Approach},

author = {Anastasios Doulamis and Athanasios Voulodimos and Nikolaos Doulamis and Sofia Soile and Anastasios Lampropoulos},

url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006347304510460},

doi = {10.5220/0006347304510460},

isbn = {978-989-758-226-4},

year  = {2017},

date = {2017-01-01},

booktitle = {Proc. 12th Int. Jt. Conf. Comput. Vision, Imaging Comput. Graph. Theory Appl.},

volume = {5},

pages = {451--460},

publisher = {SCITEPRESS - Science and Technology Publications},

abstract = {Intangible Cultural Heritage is a mainspring of cultural diversity and as such it should be a focal point in cultural heritage preservation and safeguarding endeavours. Nevertheless, although significant progress has been made in digitization technology as regards tangible cultural assets and especially in the area of 3D reconstruction, the e-documentation of intangible cultural heritage has not seen comparable progress. One of the main reasons associated lies in the significant challenges involved in the systematic e-digitisation of intangible cultural assets, such as performing arts. In this paper, we present at a high-level an approach for transforming intangible cultural assets, namely folk dances, into tangible choreographic digital objects. The approach is being implemented in the context of the H2020 European project "Terpsichore".},

keywords = {3D modelling, Cultural heritage, Folkloric dances, Intangible Cultural Heritage, Performing arts},

pubstate = {published},

tppubtype = {inproceedings}

}