Template Matching in Images using Segmented Normalized Cross-Correlation

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Main Authors:	Marušić, Davor, Popović, Siniša, Kalafatić, Zoran
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
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author	Marušić, Davor Popović, Siniša Kalafatić, Zoran
author_facet	Marušić, Davor Popović, Siniša Kalafatić, Zoran
contents	In this paper, a new variant of an algorithm for normalized cross-correlation (NCC) is proposed in the context of template matching in images. The proposed algorithm is based on the precomputation of a template image approximation, enabling more efficient calculation of approximate NCC with the source image than using the original template for exact NCC calculation. The approximate template is precomputed from the template image by a split-and-merge approach, resulting in a decomposition to axis-aligned rectangular segments, whose sizes depend on per-segment pixel intensity variance. In the approximate template, each segment is assigned the mean grayscale value of the corresponding pixels from the original template. The proposed algorithm achieves superior computational performance with negligible NCC approximation errors compared to the well-known Fast Fourier Transform (FFT)-based NCC algorithm, when applied on less visually complex and/or smaller template images. In other cases, the proposed algorithm can maintain either computational performance or NCC approximation error within the range of the FFT-based algorithm, but not both.
format	Preprint
id	arxiv_https___arxiv_org_abs_2502_01286
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Template Matching in Images using Segmented Normalized Cross-Correlation Marušić, Davor Popović, Siniša Kalafatić, Zoran Computer Vision and Pattern Recognition In this paper, a new variant of an algorithm for normalized cross-correlation (NCC) is proposed in the context of template matching in images. The proposed algorithm is based on the precomputation of a template image approximation, enabling more efficient calculation of approximate NCC with the source image than using the original template for exact NCC calculation. The approximate template is precomputed from the template image by a split-and-merge approach, resulting in a decomposition to axis-aligned rectangular segments, whose sizes depend on per-segment pixel intensity variance. In the approximate template, each segment is assigned the mean grayscale value of the corresponding pixels from the original template. The proposed algorithm achieves superior computational performance with negligible NCC approximation errors compared to the well-known Fast Fourier Transform (FFT)-based NCC algorithm, when applied on less visually complex and/or smaller template images. In other cases, the proposed algorithm can maintain either computational performance or NCC approximation error within the range of the FFT-based algorithm, but not both.
title	Template Matching in Images using Segmented Normalized Cross-Correlation
topic	Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2502.01286

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