Publications Freek Stulp

@Article{wimmer08learning,
  title                    = {Learning Local Objective Functions for Robust Face Model Fitting},
  author                   = {Matthias Wimmer and Freek Stulp and Sylvia Pietzsch and Bernd Radig},
  journal                  = {IEEE Transactions on Pattern Analysis and Machine Intelligence~(PAMI)},
  year                     = {2008},
  number                   = {8},
  volume                   = {30},
  abstract                 = {Model-based fitting has proven to be a successful approach to interpreting the large amount of information contained in images. Fitting algorithms search for the global optimum of an objective function, which should correspond to the best model fit in a given image. Although fitting algorithms have been the subject of intensive research and evaluation, the objective function is usually designed ad hoc, based on implicit and domain-dependent knowledge. This often leads to functions with many local minima, and a global minimum that does not correspond to the best model fit. We address the root of this problem by learning more robust objective functions. First, we formulate a set of desirable properties for objective functions, and give a concrete example of an ideal function that has these properties. Then, we propose a novel approach that learns an objective function from training data generated by this function and manually annotated images. In this approach, critical decisions such as the feature selection are automated, and the remaining manual steps hardly require domain-dependent knowledge. An extensive empirical evaluation demonstrates that learned objective functions enable fitting algorithms to determine the best model fit more accurately and efficiently than designed objective functions. },
  bib2html_pubtype         = {Journal},
  bib2html_rescat          = {Learning Objective Functions for Face Model Fitting},
  url                      = {http://www2.computer.org/portal/web/csdl/doi/10.1109/TPAMI.2007.70793}
}