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Gesture recognition

Gesture recognition

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Gesture recognition

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Presentation Transcript

  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