Quick Techniques for Template Matching by Normalized Cross-Correlation Method

Abstract

Object recognition is one of the fundamental challenges in signal processing, image processing and computer vision, where the goal is to identify and localize the extent of object instances within an image. A novel approach for performing the matching by normalized cross-correlation method in minimum time is introduced. The template matching by correlation is performed between template w and the image f where the template’s position is to be determined in the image. The computing process of correlation coefficient is analyzed and resolved into minute parts or units. These minute units are computed one time only before embedding them in larger blocks and stored in sum tables. The larger blocks are computed in recursive manner, using the sum tables, by adding and/or subtracting minute units from the original block instead of computing them from scratch. Moreover, this technique has been more developed by performing the cross-correlation on the odd or even signal’s samples only. The new approach, in its final form, has reduced the cross-correlation calculation time by 90%-94% depending on the image’s and template’s sizes.