Temporal feature extraction by sparse decomposition of range images using gated neurons

Abstract

A novel approach of temporal feature extraction over a predefined window at every pixel of range images is presented. Range images are first smoothed by an edge preserving non-linear filter and then decomposed into a time sequence of image frames by a gated neuronal array. The image pixels are selected via neuronal gates controlled by a spatial grating function whose parameters are the 3D distance to the center pixel of the window and the time dependent spatial grating frequency. As the frequency is varied from a maximum value down to zero over a preset discrete time period, statistical information on the set of pixels selected is gathered. This temporal information is considered to represen..