A perceptual human-machine interface based on visual appearence of hand movements is presented. Gestures are defined as the temporal evolution of 3D poses the of user's hand. Each gesture is described by five temporal sequences corresponding to the time evolution of the stereo depth information at the finger-tips. To this end feature processing directly exploits binocular disparity information considered in its full spatio-temporal dimension. Hidden Markov Models are used to represent the statistical properties of the gesture. The high recognition rate obtained by experimental testing on a five gesture alphabet validates the approach
The efficacy of anisotropic versus isotropic filtering is anayzed with respect to general phase-based metrics for early vision attributes. We verified that the spectral information content gathered through oriented frequency channels is characterized by high compactness and flexibility, since a wide range of visual attributes emerge from different hierarchical combinations of the same channels. We observed that it is preferable to construct a multichannel, multiorientation representation, rather than using a more compact representation based on an isotropic generalization of the analytic signal. The complete harmonic content is then combined in the phaseorientation space at the final stage, only, to come up with the ultimate perceptual decisions, thus avoiding an "early condensation" of basic features. The resulting algorithmic solutions reach high performance in real-world situations at an affordable computational cost.
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