Keypoint Matching Outlier Rejection, Delaunay Triangulation

asked 2015-01-29 08:42:24 -0600

updated 2015-01-29 09:31:58 -0600

Can anyone point me to an OpenCV solution or a quality paper detailing outlier rejection based upon keypoint context, i.e. the keypoints surrounding the keypoint in the 'training' or model image? Matching could make use of this information (not brute force, but with a match threshold), or it could be a post brute-force refinement operation. The complexity difference would matter as I want this to run at super frame rate. The idea is to keep only those point matches that 'agree' with each other topologically, as in a deforming mesh. It seems delaunay triangulation could be used for identifying neighbors (creating the mesh), but this API is not super clear and has been deprecated.

Any high-level algorithm design thoughts?

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could you check your link again ? cornerSubPix seems unrelated

SubDiv2d ? (sorry, only documented for 3.0)

berak ( 2015-01-29 08:59:16 -0600 )edit

My bad on the bad link, should be fixed now.

Der Luftmensch ( 2015-01-29 09:32:34 -0600 )edit

so, let's say, you have a triangle mesh:

Rect rect(0, 0, 401, 401);   
Subdiv2D subdiv(rect);   

for( size_t i = 0; i < pts.size(); i++ )
{
    subdiv.insert( pts[i].pt );        
}

and then ?

berak ( 2015-01-29 09:49:51 -0600 )edit

Here is a relevant paper.

Der Luftmensch ( 2015-01-29 21:15:21 -0600 )edit

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answered 2018-03-03 11:27:57 -0600

Der Luftmensch

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Raising from the dead, but there is now a quality solution on offer.

This question asked for a method of enforcing consistency between potential matches over local regions. The recently added Grid-based Motion Statistics does just this and is very fast.

See cv::matchGMS()

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answered 2015-10-09 07:18:20 -0600

Der Luftmensch 2
21 ●2 ●2

A different approach is to do inlier detection by finding a max clique. This is an NP-Complete problem, but there are fast approximations which appear to work well in practice.

This method is described in the following papers:

Real-Time Stereo Visual Odometry for Autonomous Ground Vehicles - Andrew Howard

Fast, Unconstrained Camera Motion Estimation from Stereo without Tracking and Robust Statistics - Heiko Hirschmu ̈ller, Peter R. Innocent and Jon M. Garibaldi

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http://answers.opencv.org/question/75...

This answer is code to perform the technique in the papers.

Der Luftmensch ( 2015-11-13 12:37:30 -0600 )edit

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Keypoint Matching Outlier Rejection, Delaunay Triangulation

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