Compact representations of stationary dynamic textures
This paper addresses the problem of modeling stationary color dynamic textures with Gaussian processes. We detail two particular classes of such processes that are parameterized by a small number of compactly supported linear filters, so-called dynamical textons (dynTextons). The first class extends previous works on the spot noise texture model to the dynamical setting. It directly estimates the dynTexton to fit a translation-invariant covariance from the exemplar. The second class is a specialization of the auto-regressive (AR) dynamic texture method to the setting of space and time stationary textures. This allows one to parameterize the process covariance using only a few linear filters. Numerical experiments on a database of stationary textures shows that the methods, despite their extreme simplicity, provide state of the art results to synthesize space stationary dynamical texture.