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Radon Transform Based Automatic Posture Recognition in Ballet Dance

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Gesture Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 724))

Abstract

The proposed system aims at automatic identification of an unknown dance posture referring to the twenty primitive postures of ballet, simultaneously measuring the proximity of an unknown dance posture to a known primitive. The proposed system aims at automatic identification of an unknown dance posture referring to the twenty 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 color segmentation is performed on the dance postures, the outputs of which are dilated and processed to generate skeletons of the original postures.

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References

  1. A. LaViers, Y. Chen, C. Belta, M. Egerstedt, Automatic sequencing of ballet poses. IEEE Robot. Autom. Mag. 18(3), 87–95 (2011)

    Article  Google Scholar 

  2. F. Guo, G. Qian, Dance posture recognition using wide-baseline orthogonal stereo cameras, in 7th International Conference on Automatic Face and Gesture Recognition, FGR 2006 (2006), pp. 481–486

    Google Scholar 

  3. P. Silapasuphakornwong, S. Phimoltares, C. Lursinsap, A. Hansuebsai, Posture recognition invariant to background, cloth textures, body size, and camera distance using morphological geometry, in 2010 International Conference on Machine Learning and Cybernetics (ICMLC), vol 3 (2010), pp. 1130–1135

    Google Scholar 

  4. K. Akita, Image sequence analysis of real world human motion. Pattern Recognit. 17(1), 73–83 (1984)

    Article  Google Scholar 

  5. Y. Guo, G. Xu, S. Tsuji, Understanding human motion patterns, in Proceedings of the 12th IAPR International. Conference on Pattern Recognition, 1994. Vol. 2-Conference B: Computer Vision & Image Processing, vol 2 (1994), pp. 325–329

    Google Scholar 

  6. C. Hu, Q. Yu, Y. Li, S. Ma, Extraction of parametric human model for posture recognition using genetic algorithm, in Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition (2000), pp. 518–523

    Google Scholar 

  7. G. Feng, Q. Lin, Design of elder alarm system based on body posture reorganization, in 2010 International Conference on Anti-Counterfeiting Security and Identification in Communication (ASID) (2010), pp. 249–252

    Google Scholar 

  8. Z. Zhou, W. Dai, J. Eggert, J.T. Giger, J. Keller, M. Rantz, Z. He, A real-time system for in-home activity monitoring of elders, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009 (2009), pp. 6115–6118

    Google Scholar 

  9. S. Saha, A. Banerjee, S. Basu, A. Konar, A.K. Nagar, Fuzzy image matching for posture recognition in ballet dance, in 2013 IEEE International Conference on Fuzzy Systems (FUZZ) (2013), pp. 1–8

    Google Scholar 

  10. A. Banerjee, S. Saha, S. Basu, A. Konar, R. Janarthanan, A novel approach to posture recognition of ballet dance, in 2014 IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT) (2014), pp. 1–5

    Google Scholar 

  11. S. Saha, S. Ghosh, A. Konar, A.K. Nagar, Gesture recognition from indian classical dance using kinect sensor, in 2013 5th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN) (2013), pp. 3–8

    Google Scholar 

  12. S. Saha, S. Ghosh, A. Konar, R. Janarthanan, Identification of Odissi dance video using Kinect sensor, in 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2013), pp. 1837–1842

    Google Scholar 

  13. S.L. Phung, A. Bouzerdoum Sr., D. Chai Sr., Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148–154 (2005)

    Article  Google Scholar 

  14. M.J. Jones, J.M. Rehg, Statistical color models with application to skin detection. Int. J. Comput. Vis. 46(1), 81–96 (2002)

    Article  MATH  Google Scholar 

  15. C.R. Dyer, Computing the Euler number of an image from its quadtree. Comput. Graph. Image Process. 13(3), 270–276 (1980)

    Article  Google Scholar 

  16. S.R. Deans, Hough transform from the Radon transform. IEEE Trans. Pattern Anal. Mach. Intell. 2, 185–188 (1981)

    Article  Google Scholar 

  17. G. Beylkin, Discrete radon transform. IEEE Trans. Acoust. Speech Signal Process. 35(2), 162–172 (1987)

    Article  MathSciNet  Google Scholar 

  18. J.P. Lewis, Fast normalized cross-correlation. Vision Interface 10(1), 120–123 (1995)

    Google Scholar 

  19. A. Chaudhary, J.L. Raheja, Bent fingers’ angle calculation using supervised ANN to control electro-mechanical robotic hand. Comput. Electr. Eng. 39(2), 560–570 (2013)

    Article  Google Scholar 

  20. S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras, Introduction to Pattern Recognition: A Matlab Approach: A Matlab Approach (Academic Press, 2010)

    Google Scholar 

  21. T.M. Mitchell, Machine learning and data mining. Commun. ACM 42(11), 30–36 (1999)

    Article  Google Scholar 

  22. S. Saha, M. Pal, A. Konar, R. Janarthanan, Neural network based gesture recognition for elderly health care using kinect sensor, in Swarm, Evolutionary, and Memetic Computing (Springer, 2013), pp. 376–386

    Google Scholar 

  23. M. Pal, S. Saha, A. Konar, A fuzzy C means clustering approach for gesture recognition in healthcare. Knee 1, C7

    Google Scholar 

  24. T.G. Dietterich, An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach. Learn. 40(2), 139–157 (2000)

    Article  Google Scholar 

  25. R.C. Veltkamp, Shape matching: similarity measures and algorithms, in SMI 2001 International Conference on Shape Modeling and Applications (2001), pp. 188–197

    Google Scholar 

  26. F. Mokhtarian, S. Abbasi, Retrieval of similar shapes under affine transform, in Proceedings of the 3rd International Conference on Visual Information and Information Systems (1999), pp. 566–574

    Google Scholar 

  27. A. Efrat, A. Itai, Improvements on bottleneck matching and related problems using geometry, in Proceedings of the 12th Annual Symposium on Computational Geometry (1996), pp. 301–310

    Google Scholar 

  28. E.M. Arkin, L.P. Chew, D.P. Huttenlocher, K. Kedem, J.S.B. Mitchell, An efficiently computable metric for comparing polygonal shapes. IEEE Trans. Pattern Anal. Mach. Intell. 13(3), 209–216 (1991)

    Article  MATH  Google Scholar 

  29. T.G. Dietterich, Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)

    Article  Google Scholar 

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Authors and Affiliations

  1. Artificial Intelligence Laboratory, Department of Electronics and Tele-Communication Engineering, Jadavpur University, Kolkata, India

    Amit Konar

  2. Artificial Intelligence Laboratory, Department of Electronics and Tele-Communication Engineering, Jadavpur University, Kolkata, India

    Sriparna Saha

Authors
  1. Amit Konar
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  2. Sriparna Saha
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Correspondence to Amit Konar .

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Konar, A., Saha, S. (2018). Radon Transform Based Automatic Posture Recognition in Ballet Dance. In: Gesture Recognition. Studies in Computational Intelligence, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-319-62212-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-62212-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62210-1

  • Online ISBN: 978-3-319-62212-5

  • eBook Packages: EngineeringEngineering (R0)

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