In his SIFT paper, why did Lowe choose to use a Hough transform rather than RANSAC to recognize clusters of 3 consistent features? (Note that RANSAC is more efficient in comparison with Hough)
Link to the paper: https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf
As it is mentioned in the paper:
Many well-known robust fitting methods, such as RANSAC or Least Median of Squares, perform poorly when the percent of inliers falls much below 50%. Fortunately, much better performance can be obtained by clustering features in pose space using the Hough transform (Hough, 1962; Ballard, 1981; Grimson 1990).
RANSAC only handles a moderate percentage of outliers without cost blowing up while many real problems have a high rate of outliers.