A vertical-horizontal-intersections feature based method for identification of bharatanatyam double hand mudra images

@article{Anami2018AVF,
  title={A vertical-horizontal-intersections feature based method for identification of bharatanatyam double hand mudra images},
  author={Basavaraj S. Anami and Venkatesh Arjunasa Bhandage},
  journal={Multimedia Tools and Applications},
  year={2018},
  volume={77},
  pages={31021-31040}
}
Bharatanatyam is an Indian classical dance, which has to be studied under an expert. [] Key Method In the first stage, acquired images of Bharatanatyam mudras are preprocessed to obtain contours of mudras using canny edge detector. In the second stage, cell features are extracted that include number of vertical and horizontal intersections of grid lines with the contours of the mudras. In the third stage, a rule based classifier is developed to classify the given image into 24 classes of mudras.
4 Citations
A Comparative Study of Suitability of Certain Features in Classification of Bharatanatyam Mudra Images Using Artificial Neural Network
TLDR
A 3-stage methodology for classification of single hand mudra images using convolutional neural network is presented and finds application in e-learning of ‘Bharatanatyam’ dance in particular and dances in general and automation of commentary during concerts.
Unravelling of Convolutional Neural Networks through Bharatanatyam Mudra Classification with Limited Data
TLDR
The results emphasize the crucial role of domain similarity of the pre-training / training datasets for improved classification accuracy and, also indicate that doubly pre-trained CNN model yield the highest accuracy.
Transfer Learning for Classifying Single Hand Gestures on Comprehensive Bharatanatyam Mudra Dataset
TLDR
This work is primarily aimed at building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from Google search engine, YouTube videos and professional artists under staged environment constraints (plain backgrounds), exploring the effectiveness of Convolutional Neural Networks and evaluating the impacts of transfer learning and double transfer learning.

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