text: // Copyright (c) 2011 libmv authors.
  //
  // Permission is hereby granted, free of charge, to any person obtaining a copy
  // of this software and associated documentation files (the "Software"), to
  // deal in the Software without restriction, including without limitation the
  // rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  // sell copies of the Software, and to permit persons to whom the Software is
  // furnished to do so, subject to the following conditions:
  //
  // The above copyright notice and this permission notice shall be included in
  // all copies or substantial portions of the Software.
  //
  // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
  // FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
  // IN THE SOFTWARE.
  
  #include "libmv/base/vector.h"
  #include "libmv/multiview/similarity_kernel.h"
  #include "libmv/multiview/robust_similarity.h"
  #include "libmv/multiview/robust_estimation.h"
  #include "libmv/numeric/numeric.h"
  
  namespace libmv {
  
  // Estimate robustly the 2d similarity matrix between two dataset of 2D point
  // (image coords space). The 2d similarity solver relies on the 2 points solution.
  double Similarity2DFromCorrespondences2PointRobust(
      const Mat &x1,
      const Mat &x2,
      double max_error,
      Mat3 *H,
      vector<int> *inliers,
      double outliers_probability)
  {
    // The threshold is on the sum of the squared errors in the two images.
    double threshold = 2 * Square(max_error);
    double best_score = HUGE_VAL;
    typedef similarity::similarity2D::kernel::Kernel KernelH;
    KernelH kernel(x1, x2);
    *H = Estimate(kernel, MLEScorer<KernelH>(threshold), inliers, 
                  &best_score, outliers_probability);
    if (best_score == HUGE_VAL)
      return HUGE_VAL;
    else
      return std::sqrt(best_score / 2.0);  
  }
  
  } // namespace libmv
  