稳健性(进化)
相关性
计算机科学
互相关
人工智能
粒子图像测速
算法
数学
统计
物理
湍流
生物化学
化学
几何学
基因
热力学
作者
Yong Lee,Fuqiang Gu,Zeyu Gong
出处
期刊:Cornell University - arXiv
日期:2021-01-01
标识
DOI:10.48550/arxiv.2112.05303
摘要
This paper presents a novel surrogate-based cross-correlation (SBCC) framework to improve the correlation performance between two image signals. The basic idea behind the SBCC is that an optimized surrogate filter/image, supplanting one original image, will produce a more robust and more accurate correlation signal. The cross-correlation estimation of the SBCC is formularized with an objective function composed of surrogate loss and correlation consistency loss. The closed-form solution provides an efficient estimation. To our surprise, the SBCC framework could provide an alternative view to explain a set of generalized cross-correlation (GCC) methods and comprehend the meaning of parameters. With the help of our SBCC framework, we further propose four new specific cross-correlation methods, and provide some suggestions for improving existing GCC methods. A noticeable fact is that the SBCC could enhance the correlation robustness by incorporating other negative context images. Considering the sub-pixel accuracy and robustness requirement of particle image velocimetry (PIV), the contribution of each term in the objective function is investigated with particles' images. Compared with the state-of-the-art baseline methods, the SBCC methods exhibit improved performance (accuracy and robustness) on the synthetic dataset and several challenging real experimental PIV cases.
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