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6 August 2002 Error analysis of image matching using a nonplanar object model
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This paper presents predicted performance for area-based image matching where two images of a non-planar object model differ by a general perspective geometric transformation. The study shows there exists a window size that will maximize or minimize certain performance parameters for a given perspective distortion and object planarity variance. The analysis also indicates that for a given perspective distortion where pitch angle is the only parameter, many performance criterion have an optimum window size if the object model is allowed to vary. The performance measures examined are expected peak value, peak-to-sidelobe ratio (PSR), probability of acquisition (PCA), and image registration error covariance. Window adaptation based on precomputed metrics is applied to extend distortion tolerance. Statistically consistent image sets are geometrically transformed by a general perspective spatial mapping using statistically consistent independent non- planar object models with arbitrary generalized autocorrelation functions. The two images are then registered through an image matching technique, the defining functions analyzed and limitations on the amount of perspective viewpoint change of an imaging system in an aerial tactical arena are given while still allowing proper image correspondence. Monte Carlo simulation verification of theoretical predictions and results are extended to a variety of common area-based image matching techniques.
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W. Bryan Bell and Venkat Devarajan "Error analysis of image matching using a nonplanar object model", Proc. SPIE 4741, Battlespace Digitization and Network-Centric Warfare II, (6 August 2002);

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