Segmenting dynamic textures with ising descriptors, ARX models and level sets

Authors:
Atiyeh Ghoreyshi;René Vidal
Affiliations:
Center for Imaging Science, Department of BME, Johns Hopkins University, Baltimore, MD;Center for Imaging Science, Department of BME, Johns Hopkins University, Baltimore, MD
Venue:
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Year:
2006

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Abstract

We present a new algorithm for segmenting a scene consisting of multiple moving dynamic textures. We model the spatial statistics of a dynamic texture with a set of second order Ising descriptors whose temporal evolution of is governed by an Auto Regressive eXogenous (ARX) model. Given this model, we cast the dynamic texture segmentation problem in a variational framework in which we minimize the spatial-temporal variance of the stochastic part of the model. This energy functional is shown to depend explicitly on both the appearance and dynamics of the scene. Our framework naturally handles intensity and texture based image segmentation as well as dynamics based video segmentation as particular cases. Several experiments show the applicability of our method to segmenting scenes using only dynamics, only appearance, and both dynamics and appearance.