A subpixel-resolution image registration algorithm based on the nonlinear projective transformation model is proposed to account for camera translation, rotation, zoom, pan, and tilt. Typically, parameter estimation techniques for rigid- body transformation require the user to manually select feature point pairs between the images undergoing registration. In this research, the block matching algorithm is used to automatically select correlated feature point pairs between two images; these features are ten used to calculate an iterative least squares estimate of the nonlinear projective transformation parameters. Since block matching is only capable of estimating accurate displacement vectors in image regions containing a large number of edges, inaccurate feature point pairs are statistically eliminated prior to computing the least squares parameter estimate. Convergence of the registration algorithm is generally achieved in several iterations. Simulations show that the algorithm estimates accurate integer- and subpixel- resolution registration parameters for similar sensor data sets such as intensity image sequence frames, as well as for dissimilar sensor images such as multimodality slices from the Visible Human Project. Through subpixel-resolution registration, integrating the registered pixels form a short sequence of low-resolution video frames generates a high- resolution video still. Experimental results are also shown in utilizing dissimilar data registration followed by vector quantization to segment tissues from multimodality Visible Human Project image slices.