Accurate and successful image registration is a key enabling technology in applications such as image fusion, matching and pattern recognition. Knowledge of registration solution quality and accuracy can help prevent an inaccurate registration from degrading or corrupting performance of downstream image processing applications. However, estimating the spatial accuracy of image registration solutions can be difficult in the absence of ground-truth information on feature content or fiducial marker correspondences. This paper presents an automated spatial registration accuracy measurement for estimating and quantifying the spatial accuracy of correlation-based image registration in the absence of ground-truth information. For correlation surfaces exhibiting a single dominant peak, the approach consists of fitting an appropriate region of the correlation surface, about the peak coefficient, with a two-dimensional Gaussian. It then uses the covariance of the Gaussian to model the registration spatial error covariance. Use of a fitted Gaussian provides an intuitive probabilistic interpretation to the registration solution; the Gaussian function value at a spatial offset from the Gaussian peak gives the likelihood of that offset value. For more complicated regions containing multiple correlation local peak values, we extend the approach to fit a Gaussian mixture model to the region and use the mixture model covariance for the spatial accuracy metric. We describe an energy-based method for choosing the model region of the correlation surface. We discuss implementation subtleties and provide perturbation methods for handling numerically illconditioned matrices. We present numerical spatial error estimation results generated from registration of real video imagery acquired from a UAV platform.
Stephen DelMarco, Helen Webb, and Victor Tom, "Automatic spatial accuracy estimation for correlation-based image registration," Proc. SPIE 10993, Mobile Multimedia/Image Processing, Security, and Applications 2019, 1099307 (Presented at SPIE Defense + Commercial Sensing: April 15, 2019; Published: 13 May 2019); https://doi.org/10.1117/12.2518183.
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