text: // Copyright (c) 2007, 2008 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/multiview/panography_kernel.h"
  #include "libmv/multiview/robust_estimation.h"
  #include "libmv/multiview/robust_panography.h"
  #include "libmv/numeric/numeric.h"
  
  namespace libmv {
  
  double HomographyFromCorrespondance2pointsRobust(const Mat &x1,
                                                   const Mat &x2,
                                                   double max_error,
                                                   Mat3 *H,
                                                   vector<int> *inliers,
                                                   double alarm_rate) {
    // The threshold is on the squared errors in one image.
    double threshold = Square(max_error);
    double best_score = HUGE_VAL;
    panography::kernel::UnnormalizedKernel kernel(x1, x2);
    MLEScorer<panography::kernel::UnnormalizedKernel> scorer(threshold);
    *H = Estimate(kernel, scorer, inliers, 
                      &best_score, alarm_rate);
    if (best_score == HUGE_VAL)
      return HUGE_VAL;
    else
      return std::sqrt(best_score);  
  }
  
  }  // namespace libmv
  