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1 Gesture recognition Using HMMs and size functions

Gesture recognition

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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. - PowerPoint PPT Presentation

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Page 1: Gesture recognition

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Gesture recognition

Using HMMs and size functions

Page 2: Gesture recognition

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Approach

Combination of HMMs (for dynamics) and size functions (for pose representation)

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Size functionsTopological representation of contours

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Measuring functions

Functions on the contour to which the size function is computed

real image

measuring function

family of lines

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Feature extraction 1

An edge map is extracted from the image

real image

edge map

… and …

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Feature extraction 2a family of measuring functions is chosen

… the szfc are computed, and their means form the feature vector

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Hidden Markov modelsFinite-state model of gestures as sequences of a small number of poses

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Four-state HMM

Gesture dynamics -> transition matrix A

Object poses -> state-output matrix C

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EM algorithm

feature matrices: collection of feature vectors along time

EM A,C

learning the model’s parameters through EM

two instances of the same gesture

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EM algorithm -> learning the model’s parameters

Learning algorithm

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Gesture classification

HMM 1

HMM 2

HMM n

the new sequence is fed to the learnt gesture’s models

they produce a likelihoodthe most likely model is chosen (if above a threshold)