MLESAC是RANSAC与OpenCV的集成

Question

是否有任何可用的代码来使用MLESAC而不是RANSAC和OpenCV来查找已知对象？

MLESAC应该比RANSAC更健壮。这里提供了一个使用示例：

http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html

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Answer 1

您可以使用MLESAC (以及PROSAC等)来自PCL库。http://docs.pointclouds.org/trunk/a02954.html我不使用PCL，所以不能举例说明。

我使用的是Karel Lebeda http://cmp.felk.cvut.cz/software/LO-RANSAC/index.xhtml的LO-RANSAC

int do_lo = 1; //local optimization
unsigned int tent_size = tentatives.size();
double err_threshold = 2.0; //in pixels
double confidence = 0.99;
int max_samples = 100000;//max trials
int inl_limit = 0;//no limit
if (tent_size >= 5)
{
  double H[3*3];
  unsigned stats[3];
  double *u2Ptr = new double[tent_size*6], *u2; u2=u2Ptr;
  typedef unsigned char uchar;
  unsigned char *inl2 = new uchar[tent_size];

  for( int i = 0; i < tentatives.size(); i++ )
    {
      *u2Ptr =  keypoints_object[ tentatives[i].queryIdx ].pt.x;
      u2Ptr++;

      *u2Ptr =  keypoints_object[ tentatives[i].queryIdx ].pt.y;
      u2Ptr++;
      *u2Ptr =  1.;
      u2Ptr++;

      *u2Ptr =   keypoints_scene[ tentatives[i].trainIdx ].pt.x;
      u2Ptr++;

      *u2Ptr =   keypoints_scene[ tentatives[i].trainIdx ].pt.y;
      u2Ptr++;
      *u2Ptr =  1.;
      u2Ptr++;
    }
  ransacH(u2, tent_size, err_threshold*err_threshold, confidence, max_samples, do_lo, , H, inl2,stats);
  for(i=0; i < tent_size; i++)
    if (inl2[i])
      inliers.push_back(good_matches[i]);

  delete[] u2; delete[] inl2;