Abstract
This chapter deals with statistical problems involving image registration from landmark data, either in Euclidean 2-space R2 or 3-space R3. In this problem, we have two images of the same object (such as satellite images taken at different times) or an image of a prototypical object and an actual object. It is desired to find the rotation, translation, and possibly scale change, which will best align the two images. Whereas many problems of this type are two-dimensional, it should be noted that medical imaging is often three dimensional.
After discussing several estimation techniques and their calculation, we discuss the relative efficiency of the various estimators. These results are important in choosing an optimal estimator. The relationship of the geometry of the landmarks to the statistical properties of the estimators is discussed. Finally we discuss diagnostics to determine which landmarks are most influential on the estimated registration. If the registration is unsatisfactory, these diagnostics can be used to determine which data points are most responsible and should be reexamined.
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Chang, T. (2023). Image Registration, Rigid Bodies, and Unknown Coordinate Systems. In: Pham, H. (eds) Springer Handbook of Engineering Statistics. Springer Handbooks. Springer, London. https://doi.org/10.1007/978-1-4471-7503-2_56
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DOI: https://doi.org/10.1007/978-1-4471-7503-2_56
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