Many studies have been made in the past for optimization using covariance matrices of feature points. We first describe how to compute the covariance matrix of a feature point from the gray levels by integrating existing methods. Then, we experimentally examine if thus computed covariance matrices really reflect the accuracy of the feature points. To test this, we do subpixel template matching and compute the homography and the fundamental matrix. Our conclusion is rather surprising, pointing out important elements often overlooked.
Do we really have to consider covariance matrices for image features?
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 2 ; 301-306 vol.2
2001-01-01
781676 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Do We Really Have to Consider Covariance Matrices for Image Features?
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