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Non Linear Temporal Textures Synthesis: A Monte Carlo Approach

  • Andrea Masiero
  • Alessandro Chiuso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)

Abstract

In this paper we consider the problem of temporal texture modeling and synthesis. A temporal texture (or dynamic texture) is seen as the output of a dynamical system driven by white noise. Experimental evidence shows that linear models such as those introduced in earlier work are sometimes inadequate to fully describe the time evolution of the dynamic scene. Extending upon recent work which is available in the literature, we tackle the synthesis using non-linear dynamical models. The non-linear model is never given explicitly but rather we describe a methodology to generate samples from the model. The method requires estimating the “state” distribution and a linear dynamical model from the original clip which are then used respectively as target distribution and proposal mechanism in a rejection sampling step. We also report extensive experimental results comparing the proposed approach with the results obtained using linear models (Doretto et al.) and the “closed-loop” approach presented at ECCV 2004 by Yuan et al.

Keywords

Nonnegative Matrix Factorization Gaussian Density Transition Kernel Texture Synthesis Kernel Density Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andrea Masiero
    • 1
  • Alessandro Chiuso
    • 1
  1. 1.Department of Information EngineeringUniversity of PadovaPadovaItaly

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