Gesture recognition
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Gesture recognition PowerPoint PPT Presentation


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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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Gesture recognition

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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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