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Edited Doxygen documentation in the module calib3d.

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Added a mention within calib3d.hpp that the flag RHO is available as an
option on calls to findHomography().
This commit is contained in:
Olexa Bilaniuk 2015-02-03 22:55:40 -05:00
parent 8907e6feac
commit 69b146412a
1 changed files with 4 additions and 3 deletions
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modules/calib3d/include/opencv2/calib3d.hpp
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@ -266,8 +266,9 @@ a vector\<Point2f\> .
- **0** - a regular method using all the points - **0** - a regular method using all the points
- **RANSAC** - RANSAC-based robust method - **RANSAC** - RANSAC-based robust method
- **LMEDS** - Least-Median robust method - **LMEDS** - Least-Median robust method
- **RHO** - PROSAC-based robust method
@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier @param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier
(used in the RANSAC method only). That is, if (used in the RANSAC and RHO methods only). That is, if
\f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \| > \texttt{ransacReprojThreshold}\f] \f[\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \| > \texttt{ransacReprojThreshold}\f]
then the point \f$i\f$ is considered an outlier. If srcPoints and dstPoints are measured in pixels, then the point \f$i\f$ is considered an outlier. If srcPoints and dstPoints are measured in pixels,
it usually makes sense to set this parameter somewhere in the range of 1 to 10. it usually makes sense to set this parameter somewhere in the range of 1 to 10.
@ -290,7 +291,7 @@ pairs to compute an initial homography estimate with a simple least-squares sche
However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective
transformation (that is, there are some outliers), this initial estimate will be poor. In this case, transformation (that is, there are some outliers), this initial estimate will be poor. In this case,
you can use one of the two robust methods. Both methods, RANSAC and LMeDS , try many different you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different
random subsets of the corresponding point pairs (of four pairs each), estimate the homography matrix random subsets of the corresponding point pairs (of four pairs each), estimate the homography matrix
using this subset and a simple least-square algorithm, and then compute the quality/goodness of the using this subset and a simple least-square algorithm, and then compute the quality/goodness of the
computed homography (which is the number of inliers for RANSAC or the median re-projection error for computed homography (which is the number of inliers for RANSAC or the median re-projection error for
@ -301,7 +302,7 @@ Regardless of the method, robust or not, the computed homography matrix is refin
inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the
re-projection error even more. re-projection error even more.
The method RANSAC can handle practically any ratio of outliers but it needs a threshold to The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to
distinguish inliers from outliers. The method LMeDS does not need any threshold but it works distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the
noise is rather small, use the default method (method=0). noise is rather small, use the default method (method=0).