Fast Template Matching Based on Normalized Cross Correlation Using Adaptive Block Partitioning and Initial Threshold Estimation

Abstract

This paper proposes a fast template matching method based on normalized cross correlation (NCC). NCC is more robust against image variations such as illumination changes than the widely-used sum of absolute difference (SAD). A problem with NCC has been its high computation cost. To deal with this problem, we use adaptive block partitioning and initial threshold estimation to extend the multilevel successive elimination algorithm. Adaptive block partitioning provides efficient sub-block partitioning and tighter boundaries. Initial threshold estimation yields a larger boundary threshold. They greatly suppress the number of search points at an earlier level from the beginning of search. The proposed method is exhaustive and robust with respect to template position and size. Experiments show that our method is up to 400 times faster than the brute force method and is significantly faster than conventional methods.