The matching of synthetically generated images of known solid objects with "real world" scenes is of fundamental importance in advanced robot vision systems. An efficient "hierarchical convolution" algorithm is presented. A scene contained in a quadtree is convolved with a matched filter quadtree derived from a synthetic image. During algorithm operation, a series of filtered images is generated at increasingly finer resolution. This allows for strategies based on coarse-to-fine matching. The algorithm is derived for both binary and gray scale images. In the gray scale case, the new algorithm is shown to require one-third fewer multiplications than conventional cross correlation.