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?