| Dynamic textures are sequences of images of dynamic scenes that exhibit some temporal regularity properties, intended in a statistical sense; these include, for example, ocean waves, smoke, whirlwind, fire, foliage, but also moving objects with a "defined shape," for instance flowers, or flags in wind etc. This work presents a characterization of this class of video sequences, and poses the problems of modeling, learning, synthesis, animation, recognition, and segmentation of dynamic textures.; Since, in absence of any additional prior knowledge, the visual reconstruction problem from images alone is ill-posed, in this work we give up trying to infer the physical model that generated the images, and analyze sequences of images solely as visual signals. We do so by building a statistical framework, and draw on disciplines like time series analysis, system, control, and identification theory.; We derive three generative models, the simplest possible, that are able to capture, respectively, the temporal second-order statistics, the spatio-temporal second-order statistics, and the higher-order temporal statistics of dynamic textures. We propose to learn model parameters in the maximum-likelihood sense, or minimum prediction error variance. We derive efficient closed-form inference procedures for learning the second-order statistics, and revert to non-linear optimization techniques for the higher-order ones. After learning a model, it can be used to extrapolate, or predict new image data both in the temporal and spatial domain. We analyze the meaning of the parameters of a model, and show how they can be manipulated to control, or animate the simulation. Using the geometry of subspaces, and statistical pattern recognition theory we derive a technique to discriminate between models, and assess the potential for building a recognition system. Finally, by combining these results with a variational framework, we design a region-based segmentation system able to partition a video sequence into regions characterized by different spatio-temporal statistics. |
