@poster{2624,
  title = {Implicit Wiener series for capturing higher-order interactions in 
  images},
  journal = {Sensory coding and the natural environment},
  abstract = {The information about the objects in an image is almost exclusively
  described by the higher-order interactions of its pixels.  The Wiener
  series is one of the standard methods to systematically characterize
  these interactions. However, the classical estimation method of the
  Wiener expansion coefficients via cross-correlation suffers from
  severe problems that prevent its application to high-dimensional and
  strongly nonlinear signals such as images. We propose an estimation
  method based on regression in a reproducing kernel Hilbert space that
  overcomes these problems using polynomial kernels as known from
  Support Vector Machines and other kernel-based methods. Numerical
  experiments show performance advantages in terms of convergence,
  interpretability and system sizes that can be handled. By the time of
  the conference, we will be able to present first results on the
  higher-order structure of natural images.},
  editors = {Olshausen, B.A. and M. Lewicki},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2004},
  slug = {2624},
  author = {Franz, MO. and Sch{\"o}lkopf, B.}
}