International Journal of Computer Vision

, Volume 80, Issue 3, pp 375–405

Building Blocks for Computer Vision with Stochastic Partial Differential Equations


    • Center of Complex Systems and VisualizationBremen University
  • Hanno Scharr
    • Institute for Chemistry and Dynamics of the Geosphere, Institute 3: PhytosphereForschungszentrum Juelich GmbH
  • Kai Krajsek
    • Institute for Chemistry and Dynamics of the Geosphere, Institute 3: PhytosphereForschungszentrum Juelich GmbH
  • Robert M. Kirby
    • School of Computing and Scientific Computing and Imaging InstituteUniversity of Utah

DOI: 10.1007/s11263-008-0145-5

Cite this article as:
Preusser, T., Scharr, H., Krajsek, K. et al. Int J Comput Vis (2008) 80: 375. doi:10.1007/s11263-008-0145-5


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.


Image processingError propagationRandom fieldsPolynomial chaosStochastic partial differential equationsStochastic Galerkin methodStochastic finite element method
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© Springer Science+Business Media, LLC 2008