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Gesture recognition

Gesture recognition. Using HMMs and size functions. Approach. Combination of HMMs (for dynamics) and size functions (for pose representation). Size functions. Topological representation of contours. Measuring functions. Functions on the contour to which the size function is computed.

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Gesture recognition

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  1. Gesture recognition Using HMMs and size functions

  2. Approach • Combination of HMMs (for dynamics) and size functions (for pose representation)

  3. Size functions • Topological representation of contours

  4. Measuring functions • Functions on the contour to which the size function is computed real image family of lines measuring function

  5. Feature extraction 1 • An edge map is extracted from the image real image edge map • … and …

  6. Feature extraction 2 • a family of measuring functions is chosen • … the szfc are computed, and their means form the feature vector

  7. Hidden Markov models • Finite-state model of gestures as sequences of a small number of poses

  8. Four-state HMM • Gesture dynamics -> transition matrix A • Object poses -> state-output matrix C

  9. EM algorithm • feature matrices: collection of feature vectors along time • two instances of the same gesture A,C EM • learning the model’s parameters through EM

  10. Learning algorithm • EM algorithm -> learning the model’s parameters

  11. Gesture classification • the new sequence is fed to the learnt gesture’s models • they produce a likelihood • the most likely model is chosen (if above a threshold) HMM 1 HMM 2 … HMM n

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