Building Blocks for Computer Vision with Stochastic Partial Differential Equations

  • Authors:

  • Tobias Preusser;Hanno Scharr;Kai Krajsek;Robert M. Kirby

  • Affiliations:

  • Center of Complex Systems and Visualization, Bremen University, Bremen, Germany;Institute for Chemistry and Dynamics of the Geosphere, Institute 3: Phytosphere, Forschungszentrum Juelich GmbH, Juelich, Germany;Institute for Chemistry and Dynamics of the Geosphere, Institute 3: Phytosphere, Forschungszentrum Juelich GmbH, Juelich, Germany;School of Computing and Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2008

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Abstract

We discuss the basic concepts of computer vision with stochastic partial differential equations (SPDEs). In typical approaches based on partial differential equations (PDEs), the end result in the best case is usually one value per pixel, the "expected" value. Error estimates or even full probability density functions PDFs are usually not available. This paper provides a framework allowing one to derive such PDFs, rendering computer vision approaches into measurements fulfilling scientific standards due to full error propagation. We identify the image data with random fields in order to model images and image sequences which carry uncertainty in their gray values, e.g. due to noise in the acquisition process.The noisy behaviors of gray values is modeled as stochastic processes which are approximated with the method of generalized polynomial chaos (Wiener-Askey-Chaos). The Wiener-Askey polynomial chaos is combined with a standard spatial approximation based upon piecewise multi-linear finite elements. We present the basic building blocks needed for computer vision and image processing in this stochastic setting, i.e. we discuss the computation of stochastic moments, projections, gradient magnitudes, edge indicators, structure tensors, etc. Finally we show applications of our framework to derive stochastic analogs of well known PDEs for de-noising and optical flow extraction. These models are discretized with the stochastic Galerkin method. Our selection of SPDE models allows us to draw connections to the classical deterministic models as well as to stochastic image processing not based on PDEs. Several examples guide the reader through the presentation and show the usefulness of the framework.