Abstract
Mudras are considered as spiritual gestures in the religious sense and hold a very important place in the cultural and spiritual space in India. Images are the symbolic representations of divinity in religious artwork and their origins are conveyed through the religions and spiritual beliefs. Such gestures also have some specific meaning in the Buddhist religion. It refers to some of the events in the life of Buddha or denotes special characteristics of the Buddha deities. In recent years, automatic identification of these gestures has gained a greater interest from the machine learning community. This would help to identify the various deities that exist in the Buddhist religion, leading to digital preservation of cultural heritage art. This paper provides a framework that recognizes the Buddhist hand gesture or Hasta Mudra. The morphological features are extracted from the gesture employing geometric parameters. The experimental results show that utilising geometric features and using k-Nearest Neighbor (kNN) as a classifier, an approximately 70% recognition rate is achieved.
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References
Liu, H., Wang, W., Xie, H.: Thangka image inpainting using adjacent information of broken area. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, IMECS 2008, Hong Kong, vol. 1 (2008)
Hu, W., Li, Z., Liu, Z.: An improved morphological component analysis algorithm for tangka image inpainting. In: 6th International Congress on Image and Signal Processing (CISP), vol. 1, pp. 346–351. IEEE (2013)
Liu, X.: A survey on thangka image inpainting method based on structure-borne (2015)
Yin, L., Wang, W.: Headdress detection based on saliency map for thangka portrait image. In: MVA2011 IAPR Conference on Machine Vision Applications, Nara, Japan (2011)
Zhang, W., Lin, S.: Research on Tibet Tangka based on shape grammar. In: 9th International Conference on Computer-Aided Industrial Design and Conceptual Design, CAID/CD 2008, pp. 373–376. IEEE (2008)
Yin, L., Wang, W., Yang, D.: Study on how to distinguish Thangka and non-Thangka image. In: International Multi Conference of Engineers and Computer Scientists, Hong Kong, vol. 2, pp. 1476–1480 (2010)
Fei, X., Liu, C.: Multi-granular method for retrieving Thangka images. In: International Conference on Progress in Informatics and Computing (PIC). IEEE (2014)
Bhaumik, G., Samaddar, S.G., Samaddar, A.B.: An algorithm for digital authentication of Buddha painting on Thangka. Sci. Cult. 84(34), 129–133 (2018)
Devi, M., Saharia, S., Bhattacharyya, D.K.: Dance gesture recognition: a survey. Int. J. Comput. Appl. 122(5) (2015)
Mozarkar, S.: Recognizing Bharatnatyam Mudra using principles of gesture recognition. Int. J. Comput. Sci. Netw. 2, 2277–5420 (2013)
Fujiyoshi, H., Lipton, A.J., Kanade, T.: Real-time human motion analysis by image skeletonization. IEICE Trans. Inf. Syst. 87(1), 113–120 (2004)
Bunce, F.W.: Mudras (Hastas). Mudras in Buddhist and Hindu Practices-An Iconographic Consideration. D.K PrintWorld (P) Ltd., New Delhi (2017) ISBN 9788124603123
Arora, A., Gupta, A., Bagmar, N., Mishra, S., Bhattacharya, A.: A plant identification system using shape and morphological features on segmented leaflets. In: CLEF. Team IITK (2012)
Manik, F.Y., Herdiyeni, Y., Herliyana, E.N.: Leaf morphological feature extraction of digital image anthocephalus cadamba. Telkomnika (Telecommun. Comput. Electron. Control) 14(2), 630–637 (2016)
Kumar, K.V.V., Kishore, P.V.V.: Indian classical dance mudra classification using HOG features and SVM classifier. In: Satapathy, S.C., Bhateja, V., Das, S. (eds.) Smart Computing and Informatics. SIST, vol. 77, pp. 659–668. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5544-7_65
Futane, P.R., Dharaskar, R.V.: Hasta Mudra: an interpretation of Indian sign hand gestures. In: Electronics Computer Technology (ICECT), Vol. 2, pp. 377–380. IEEE (2011)
Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)
Ye, J., Janardan, R., Li, Q.: Two-dimensional linear discriminant analysis. In: Advances in Neural Information Processing Systems, pp. 1569–1576 (2005)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
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Bhaumik, G., Govil, M.C. (2020). Buddhist Hasta Mudra Recognition Using Morphological Features. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1240. Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7_29
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DOI: https://doi.org/10.1007/978-981-15-6315-7_29
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