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Gesture Recognition by Attention Control Method for Intelligent Humanoid Robot

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3681))

Abstract

In this paper, we describe an algorithm which can automatically recognize human gesture for Human-Robot interaction by utilizing attention control method. In early works, many systems for recognizing human gestures work under many restricted conditions. To solve the problem, we propose a novel model called APM(Active Plane Model),which can represent 3D and 2D gesture information simultaneously. Also we present the state transition algorithm for selection of attention. In the algorithm, first we obtain the information about 2D and 3D shape by deforming the APM, and then the feature vectors are extracted from the deformed APM. The next step is constructing a gesture space by analyzing the statistical information of training images with PCA. And then, input images are compared to the model and individually symbolized to one of the pose model in the space. In the last step, the symbolized poses are recognized with HMM as one of model gestures. The experimental results show that the proposed algorithm is very efficient to construct intelligent interface system.

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References

  1. Huang, F.J., Chen, T.: Tracking of Multiple Faces for Human-Computer Interfaces and Virtual Environments. In: IEEE Intl. Conf. on Multimedia and Expo., July 2000, pp. 1563–1566 (2000)

    Google Scholar 

  2. Point Grey Inc., http://www.ptgrey.com

  3. Davis, J.: Recognizing Movement using Motion Histograms, MIT Media Lab. Technical Report No. 487 (March 1999)

    Google Scholar 

  4. Cutler, R., Turk, M.: View-based Interpretation of Real-time Optical Flow for Gesture Recognition. In: Third IEEE International Conf. on Automatic Face and Gesture Recognition (1998)

    Google Scholar 

  5. Lee, C.-W., Lee, H.-J., Yoon, S.H., Kim, J.H.: Gesture Recognition in Video Image with Combination of Partial and Global Information. In: Proc. of VCIP, Lugano, July 2003, pp. 458–466 (2003)

    Google Scholar 

  6. Kobayashi, H., Kobayashi, H.: An efficient forward-backward algorithm for an explicitduration hidden Markov model. In: Signal Processing Letters, January 2003, vol. 10(1), pp. 11–14. IEEE, Los Alamitos (2003)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Oh, J.Y., Lee, C.W., You, B.J. (2005). Gesture Recognition by Attention Control Method for Intelligent Humanoid Robot. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_162

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  • DOI: https://doi.org/10.1007/11552413_162

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28894-7

  • Online ISBN: 978-3-540-31983-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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