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Applications of Computer Vision, IEEE Workshop on (1996)
Sarasoto, FL
Dec. 2, 1996 to Dec. 4, 1996
ISBN: 0-8186-7620-5
pp: 70
Simon Moss , University of York.
Edwin R. Hancock , University of York.
This paper describes an application of the EM (expectation and maximization) algorithm to the registration of incomplete millimetric radar images. The data used in this study consists of a series of non-overlapping radar sweeps. Our registration process aims to recover transformation parameters between the radar-data and a digital map. The tokens used in the matching process are fragmented line-segments extracted from the radar images which predominantly correspond to hedge-rows in the cartographic data. The EM technique models data uncertainty using Gaussian mixtures defined over the positions and orientations of the lines. The resulting weighted least-squares parameter estimation problem is solved using the Levenberg-Marquardt method. A sensitivity analysis reveals that the data-likelihood function is unimodal in the translation and scale parameters. In-fact the algorithm is only sensitive to the choice of initial rotation parameter; this is attributable to local sub-optima in the log-likelihood function associated with 90 degrees orientation ambiguities in the map. The method is also demonstrated to be relatively insensitive to random measurement errors on the line-segments.
Millimetre radar images, hedge detection, EM algorithm, Cartographic matching.

S. Moss and E. R. Hancock, "Cartographic Matching with Millimetre Radar Images," Applications of Computer Vision, IEEE Workshop on(WACV), Sarasoto, FL, 1996, pp. 70.
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