Open AccessTopomorphological approach to automatic posture recognition in ballet dance
Author(s) -
Saha Sriparna,
Konar Amit
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0622
Subject(s) - dance , artificial intelligence , computer science , computer vision , line (geometry) , segmentation , euler's formula , correctness , ballet , identification (biology) , mathematics , algorithm , geometry , mathematical analysis , art , botany , literature , biology
The proposed system aims at automatic identification of an unknown dance posture referring to the 20 primitive postures of ballet, simultaneously measuring the proximity of an unknown dance posture to a known primitive. A simple and novel six stage algorithm achieves the desired objective. Skin colour segmentation is performed on the dance postures, the output of which is dilated and is processed to generate skeletons of the original postures. The stick figure diagrams laden with minor irregularities are transubstantiated to generate their affirming minimised skeletons. Each of the 20 postures based on their corresponding Euler number are categorised into five groups. Simultaneously the line integral plots of the dance primitives are determined by performing Radon transform on the minimised skeletons. The line integral plots of the fundamental postures along with their Euler number populate the initial database. The group of an unknown posture is determined based on its Euler number, while successively the unknown posture's line integral plot is compared with the line integral plots of the postures belonging to that group. An empirically determined threshold finally decides on the correctness of the performed posture. While recognising unknown postures, the proposed system registers an overall accuracy of 91.35%.