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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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Presentation Transcript
gesture recognition

Gesture recognition

Using HMMs and size functions

approach
Approach
  • Combination of HMMs (for dynamics) and size functions (for pose representation)
size functions
Size functions
  • Topological representation of contours
measuring functions
Measuring functions
  • Functions on the contour to which the size function is computed

real image

family of lines

measuring function

feature extraction 1
Feature extraction 1
  • An edge map is extracted from the image

real image

edge map

  • … and …
feature extraction 2
Feature extraction 2
  • a family of measuring functions is chosen
  • … the szfc are computed, and their means form the feature vector
hidden markov models
Hidden Markov models
  • Finite-state model of gestures as sequences of a small number of poses
four state hmm
Four-state HMM
  • Gesture dynamics -> transition matrix A
  • Object poses -> state-output matrix C
em algorithm
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
learning algorithm
Learning algorithm
  • EM algorithm -> learning the model’s parameters
gesture classification
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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