ZNCC-based Template Matching using Bounded Partial Correlation

This paper describes a class of algorithms enabling efficient and exhaustive matching of a template into an image based on the Zero mean Normalized Cross-Correlation function (ZNCC). The approach consists in checking at each image position two sufficient conditions obtained at a reduced computational cost. This allows to skip rapidly most of the expensive calculations required to evaluate the ZNCC at those image points that cannot improve the best correlation score found so far. The algorithms shown in this paper generalize and extend the concept of Bounded Partial Correlation (BPC), previously devised for a template matching process based on the Normalized Cross-Correlation function (NCC).