01693nas a2200313   4500000000100000000000100001008004100002260006400043653003600107653001700143653001600160653003300176653003400209653002700243653002400270653001800294653004000312653002800352100002900380700002800409700002400437700002200461245008000483856016600563300001200729490000900741520060900750020002001359       2017                            d  bInternational Society for Photogrammetry and Remote Sensing10aClassification (of information)10aDepth camera10aFolk dances10aIntangible cultural heritage10aIntangible cultural heritages10aMusculoskeletal system10aPattern recognition10aSkeleton data10aThree dimensional computer graphics10aUnsupervised clustering1 aEftychios Protopapadakis1 aAthina Grammatikopoulou1 aAnastasios Doulamis1 aNikos Grammalidis00aFolk dance pattern recognition over depth images acquired via kinect sensor  uhttps://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  a587-5930 v42-23 aThe 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.  a16821750 (ISSN)