Adaptive-complexity registration of images

We present a framework for image registration algorithms that finds a lowest-order model of the flow between two images. Low-order models are useful in image registration, because they leave scene structure intact. But in real images complexity varies, and cannot be determined ahead of time. Algorithms in our framework adapt model complexity to image data during a coarse-fine parameter estimation process. Complexity increases keep residual flow small enough that motion can be correctly estimated at each subsequent resolution level. We present one algorithm within this framework which increases complexity by replacing global estimates with estimates over successively smaller patches. We show results of applying this algorithm to the task of mosaicing panoramic aerial images with unknown lens distortion and unknown camera position