Our goal is a highly-reliable, fully-automated image registration technique that takes two images and correctly aligns them or decides that they can not be aligned. The technique should handle image pairs having low overlap, variations in scale, large illumination differences (e.g. day and night), substantial scene changes, and different modalities. Our approach is a combination of algorithms for initialization, estimation and refinement, and decision-making. It starts by extracting and matching keypoints. Rank-ordered matches are tested individually in succession. Each is used to generate a similarity transformation estimate in a small region of each image surrounding the matched keypoints. A generalization of the recently developed Dual-Bootstrap algorithm is then applied to generate an image-wide transformation estimate through a combination of matching and reestimation, model selection, and region growing, all driven by a new multiscale feature extraction technique. After convergence of the Dual-Bootstrap, the transformation is accepted if it passes a correctness test that combines measures of accuracy, stability and non-randomness; otherwise the process starts over with the next keypoint match. Experimental results on a suite of challenging image pairs shows the effectivenss of the complete system.