Normalized cross correlation computation for geometry image features

Normalized cross correlation operator (NCCO) was used for pattern matching to localize popular cross-shaped features in microscopic vision. Distribution feature of similarity function around peak zone was found to be four symmetry hyperboloid planes. Inspired by this, some research work on pattern matching of geometry image features (such as rectangular, circular, etc.) was presented. A probability distribution based formula computing NCCO with two binary images was proposed. Mathematic models of some typical geometry image features (rectangular, circular, cross-type, box-type) similar functions around peak point were derived from and proved. Based on these models, some experiments about template image optimum and feature size optimum were conducted on a microscopic vision workcell. Conclusions above are practically useful to image positioning, image calibration and image tracking techniques.