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

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 nonlinear 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 represent the surface characteristics over the window. In this paper, as a first step it is demonstrated by using realistic range data that the entropy information of the sequence of images at every pixel can be effectively used to identify the edges