1 January 1999 Analysis of area-based image matching under perspective distortion for a planar object model
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This paper presents predicted performance for twodimensional cross correlation where two images taken from a 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. The analysis also indicates many performance criteria have an optimum window size if the geometric distortion includes rotation or scale, but for a given perspective distortion where pitch angle is the only parameter, these measures are not appreciably optimized by any given correlation window size. The performance measures examined are expected peak value, peak-to-sidelobe ratio, probability of correct acquisition (PCA) and false acquisition (PFA), registration error covariance, and average signal-to-noise ratio. The results use statistically consistent image models with arbitrary autocorrelation functions. Monte Carlo simulation verification of theoretical predictions is performed and results are extended to a variety of common area-based image matching techniques.
W. Bryan Bell, W. Bryan Bell, Venkat Devarajan, Venkat Devarajan, Steven J. Apollo, Steven J. Apollo, } "Analysis of area-based image matching under perspective distortion for a planar object model," Journal of Electronic Imaging 8(1), (1 January 1999). https://doi.org/10.1117/1.482714 . Submission:


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