@Article{ngo:tcva:2009,
  author    = {Ngo, Trung, Thanh and Nagahara, Hajime and Sagawa, Ryusuke and Mukaigawa, Yasuhiro and Yachida, Masahiko and Yagi, Yasushi},
  title     = {Highly Robust Estimator Using a Casedependent Residual Distribution Model},
  journal   = {IPSJ Transactions on Computer Vision and Applications},
  year      = {2009},
  volume    = {1},
  pages     = {260--276},
  month     = {November},
  doi       = {10.2197/ipsjtcva.1.260},
  url       = {http://omilab.naist.jp/\textasciitilde mukaigawa/papers/TCVA2009-RobustEstimator.pdf},
  abstract  = {The latest robust estimators usually take advantage of density estimation, such as kernel density estimation, to improve the robustness of inlier detection.
However, the challenging problem for these systems is choosing the suitable smoothing parameter, which can result in the population of inliers being over-
or under-estimated, and this, in turn, reduces the robustness of the estimation. To solve this problem, we propose a robust estimator that estimates an accurate
inlier scale. The proposed method first carries out an analysis to figure out the residual distribution model using the obvious case-dependent constraint, the
residual function. Then the proposed inlier scale estimator performs a global search for the scale producing the residual distribution that best fits the residual
distribution model. Knowledge about the residual distribution model provides a major advantage that allows us to estimate the inlier scale correctly, thereby
improving the estimation robustness. Experiments with various simulations and real data are carried out to validate our algorithm, which shows certain
benefits compared with several of the latest robust estimators.},
  publisher = {Information Processing Society of Japan}
}