Incremental B-spline deformation model for geometric graph matching

We propose a B-spline deformation model, which can be efficiently updated from sequential measurements. Our incremental update is based on the Kalman filtering, is optimal in the least squares sense, and is very fast, an order of magnitude faster than the direct methods, while converging to the same solution. While this method can be applied to any multidimensional scattered data interpolation task, our main application is a geometric graph matching algorithm, which is used for registering large images from the biomedical domain or remote sensing, based on matching linear structures such as blood vessels or roads. The B-spline transformation model needs to be updated incrementally as more points are added to the match and using the proposed Kalman-like update yields substantial speed gains over the previously used bi-Lipschitz model.