Frank R. Schmidt

Many challenging computer vision problems can be formulated as a multilinear model. Classical methods like principal component analysis use singular value decomposition to infer model parameters. Although it can solve a given problem easily if all measurements are known this prerequisite is usually violated for computer vision applications. In the current work, a standard tool to estimate singular vectors under incomplete data is reformulated as an energy minimization problem. This admits for a simple and fast gradient descent optimization with guaranteed convergence. Furthermore, the energy function is generalized by introducing an L2-regularization on the parameter space. We show a quantitative and qualitative evaluation of the proposed approach on an application from structure-from-motion using synthetic and real image data, and compare it with other works.

@InProceedings\{SAR11,
  author       = "Schmidt, Frank R. and Ackermann, Hanno and Rosenhahn, Bodo",
  title        = "Multilinear Model Estimation with L2-Regularization",
  booktitle    = "Pattern Recognition (Proc. DAGM)",
  series       = "LNCS",
  volume       = "6835",
  pages        = "81--90",
  month        = "Aug",
  year         = "2011",
  publisher    = "Springer",
  address      = "Frankfurt, Germany",
  url          = "http://frank-r-schmidt.de/Publications/2011/SAR11"
}