Abstract
The task of determining the mapping between a pair of images is called image matching and is fundamental in image processing. Prior information is essential to the inference of the mapping because the image features on which matching is based are sparsely distributed and, consequently, underconstrain the problem. In this paper, we describe the Bayesian approach to image matching and introduce suitable priors based on idealized models of continua.
This work was supported in part by the U.S.P.H.S. under grant l-ROl-NS-33662-OlAl.
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Gee, J.C., Peralta, P.D. (1996). Continuum Models for Bayesian Image Matching. In: Hanson, K.M., Silver, R.N. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 79. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5430-7_13
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DOI: https://doi.org/10.1007/978-94-011-5430-7_13
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