image-processing - RANSAC 算法

Question

谁能告诉我如何使用 RANSAC 算法在两个具有一定重叠部分的图像中选择共同特征点？问题来自基于特征的图像拼接。

Answer 1

几年前我实现了一个图像拼接器。Wikipedia 上有关 RANSAC 的文章很好地描述了一般算法。

当使用 RANSAC 进行基于特征的图像匹配时，您想要的是找到最能将第一张图像转换为第二张图像的转换。这将是维基百科文章中描述的模型。

如果您已经获得了两幅图像的特征，并且发现第一幅图像中的哪些特征与第二幅图像中的哪些特征最匹配，那么将使用类似这样的 RANSAC。

The input to the algorithm is:
n - the number of random points to pick every iteration in order to create the transform. I chose n = 3 in my implementation.
k - the number of iterations to run
t - the threshold for the square distance for a point to be considered as a match
d - the number of points that need to be matched for the transform to be valid
image1_points and image2_points - two arrays of the same size with points. Assumes that image1_points[x] is best mapped to image2_points[x] accodring to the computed features.

best_model = null
best_error = Inf
for i = 0:k
  rand_indices = n random integers from 0:num_points
  base_points = image1_points[rand_indices]
  input_points = image2_points[rand_indices] 
  maybe_model = find best transform from input_points -> base_points

  consensus_set = 0
  total_error = 0
  for i = 0:num_points
    error = square distance of the difference between image2_points[i] transformed by maybe_model and image1_points[i]
    if error < t
      consensus_set += 1
      total_error += error

  if consensus_set > d && total_error < best_error
    best_model = maybe_model
    best_error = total_error

最终结果是将 image2 中的点最好地转换为 image1 的转换，这正是您在拼接时想要的。