Condition theory for image registration and post-registration error estimation
We present in this chapter applications of condition theory for image registration problems in a general framework that is easily adapted to a variety of image processing tasks. After summarizing the history and foundations of condition theory, a short analysis is given of computational sensitivity for point correspondence between images with respect to translation, rotation-scale-translation (RST), and affine pixel transforms. Several surprising results follow from this analysis, including the principal result that increasing transform complexity is mirrored by increasing computational sensitivity, i.e., KTrans ≤ KRST ≤ KAffine . The utility of condition-based corner detectors is also seen in the demonstrated equivalence between the translational condition number and the commonly used Shi-Tomasi corner function. These results are supplemented by a short discussion of sensitivity estimation for the computed transform parameters and any resulting registration misalignment.
“Condition theory for image registration and post-registration error estimation”,
Image Registration for Remote Sensing edited by Jacqueline LeMoigne, Nathan Netanyahu and Roger Eastman, pp. 200-214, Cambridge University Press, 2011.
Node ID: 594 , DB ID: 404 , Lab: VRL , Target: Book