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快速傅里叶变换的归一化互相关计算。

Computation of the normalized cross-correlation by fast Fourier transform.

机构信息

Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, United States of America.

出版信息

PLoS One. 2018 Sep 20;13(9):e0203434. doi: 10.1371/journal.pone.0203434. eCollection 2018.

Abstract

The normalized cross-correlation (NCC), usually its 2D version, is routinely encountered in template matching algorithms, such as in facial recognition, motion-tracking, registration in medical imaging, etc. Its rapid computation becomes critical in time sensitive applications. Here I develop a scheme for the computation of NCC by fast Fourier transform that can favorably compare for speed efficiency with other existing techniques and may outperform some of them given an appropriate search scenario.

摘要

归一化互相关(NCC),通常是其 2D 版本,在模板匹配算法中经常遇到,例如人脸识别、运动跟踪、医学成像中的配准等。在时间敏感的应用中,其快速计算变得至关重要。在这里,我提出了一种通过快速傅里叶变换计算 NCC 的方案,该方案在速度效率上可以与其他现有技术相媲美,并且在适当的搜索场景下可能优于其中一些技术。

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