Maximum-Likelihood Template Matching

An improved algorithm for detecting gray-scale and binary templates in digitized images has been devised. The greatest difference between this algorithm and prior template-detecting algorithms stems from the measure used to determine the quality or degree of match between a template and given portion of an image. This measure is based on a maximum-likelihood formulation of the template- matching problem; this measure, and the matching performance obtained by use of it, are more robust than are those of prior template-matching algorithms, most of which utilize a sum-of-squared-differences measure. Other functions that the algorithm performs along with template matching include subpixel localization, estimation of uncertainty, and optimal selection of features. This algorithm is expected to be useful for detecting templates in digital images in a variety of applications, including recognition of objects, ranging by use of stereoscopic images, and tracking of moving objects or features. (For the purpose of tracking, features or objects recognized in an initial image could be used as templates for matching in subsequent images of the same scene.)