Valid data based normalized cross-correlation (VDNCC) for topography identification

The Normalized Cross-Correlation (NCC) function is a widely used pattern-matching method. However, when the input data have a void area created by non-rectangular data or outliers, the accuracy of the standard NCC function may decrease. Especially when the regional mean values under the NCC window have a significant difference in the global mean value, the possible mis-matching may affect the identification results. In this paper, a valid data based NCC (VDNCC) algorithm is proposed for eliminating the effect of the void area. The new algorithm prevents void areas from being included in the calculation by introducing the valid data templates. VDNCC obtains higher NCC values and probabilities of correct matching in the experiments. In the ballistics identification tests, the results show that VDNCC can enhance the capacity of identification based on the NCC function as the core.