Using probabilistic domain knowledge to reduce the expected computational cost of template matching

Matching of two digital images is computationally expensive, because it requires a pixel-by-pixel comparison of the pixels in the image and in the template. If we have probabilistic models for the classes of images being matched, we can reduce the expected computational cost of matching by comparing the pixels in an appropriate order. In this paper we show that the expected cumulative error when matching an image and a template is maximized by using an ordering technique. We also present experimental results for digital images, when we know the probability densities of their gray levels, or more generally, the probability densities of arrays of local property values derived from the images.