@inproceedings{4097,
  keywords = {Classification (of information), Depth camera, Folk dances, Intangible cultural heritage, Intangible cultural heritages, Musculoskeletal system, Pattern recognition, Skeleton data, Three dimensional computer graphics, Unsupervised clustering},
  author = {Eftychios Protopapadakis and Athina Grammatikopoulou and Anastasios Doulamis and Nikos Grammalidis},
  title = {Folk dance pattern recognition over depth images acquired via kinect sensor},
  abstract = {The possibility of accurate recognition of folk dance patterns is investigated in this paper. System inputs are raw skeleton data, provided by a low cost sensor. In particular, data were obtained by monitoring three professional dancers, using a Kinect II sensor. A set of six traditional Greek dances (without their variations) consists the investigated data. A two-step process was adopted. At first, the most descriptive skeleton data were selected using a combination of density based and sparse modelling algorithms. Then, the representative data served as training set for a variety of classifiers.},
  year = {2017},
  booktitle = {Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. - ISPRS Arch.},
  volume = {42-2},
  pages = {587-593},
  publisher = {International Society for Photogrammetry and Remote Sensing},
  school = {International Society for Photogrammetry and Remote Sensing},
  isbn = {16821750 (ISSN)},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021761345&doi=10.5194%2fisprs-archives-XLII-2-W3-587-2017&partnerID=40&md5=970340ae2c477b04242b161b10d28a91},
  doi = {10.5194/isprs-archives-XLII-2-W3-587-2017},
  note = {Issue: 2W3 Journal Abbreviation: Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. - ISPRS Arch.},
  language = {English},
}