Abstract
This paper describes a new method to calculate the spatio-temporal correlation efficiently in a parametric eigenspace representation for moving object recognition. A parametric eigenspace compactly represents the temporal change of an image sequence by a trajectory in the eigenspace. This representation reduces the computational cost of correlation-based comparison between image sequences. Experiments for human gait analysis and lip reading show this method is computationally useful for motion analysis and recognition.
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