This research is focused on the study of local-type image registration techniques, which concern pixel mapping between two correlated images with both global and local deformations. Our algorithm is object-contour-based, meaning that control points that conduct the transformation are adaptively selected from the matched contour points. It is novel in two important respects. First, we propose to match object contours in the source and target images by using locally-maximal-curvature points and curve projection. Second, the selection of control points is capable of adapting to both global and local deformations, normally being denser in contour segments with substantial local deformations. The proposed scheme is also hybrid in the sense that local contour matching and global surface-spline fitting of control points are combined. Simulations and some comparisons are made by using infrared thermographs and ordinary gray images. Experiments show better registration accuracy of object contours than with traditional methods such as elastic or curvature scale space matching.