SIFT-aided path-independent digital image correlation accelerated by parallel computing

作     者：Yang, Junrong Huang, Jianwen Jiang, Zhenyu Dong, Shoubin Tang, Liqun Liu, Yiping Liu, Zejia Zhou, Licheng 

作者机构：South China Univ Technol Sch Civil Engn & Transportat State Key Lab Subtrop Bldg Sci Guangzhou 510640 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Peoples R China 

出 版 物：《OPTICS AND LASERS IN ENGINEERING》 (工程光学与激光)

年 卷 期：2020年第127卷第0期

页      面：105964-000页

核心收录：

学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学] 

基　　金：National Natural Science Foundation of China [11772131, 11772132, 11772134, 11972162, 11932007] Natural Science Foundation of Guangdong Province [2015A030308017] Innovation Fund of High-end Scientific Research Institutions of Zhongshan City [2019AG031] 

主　　题：Digital image correlation Initial guess Scale-invariant feature transform Parallel computing Graphics processing unit 

摘      要：Current iterative digital image correlation (DIC) algorithms can efficiently converge at the deformation vector with high accuracy when they are fed with reliable initial guess. Thus, the adaptability of DIC method is dominated to a large extent by the estimation of initial guess. In recent years, image feature-based technique, especially the scale-invariant feature transform (SIFT), was introduced to DIC for the estimation of initial guess in the case of large and complex deformation, due to its robustness in handling the images with translation, rotation, scaling, and localized distortion. However, feature extraction and matching in SIFT are very time consuming, which limits the applications of the SIFT-aided DIC. In this study, we developed a SIFT-aided path-independent DIC method and accelerated it by introducing the parallel computing on graphics processing unit (GPU) or multi-core CPU. In our method, SIFT features are used to estimate the initial guess for the inverse compositional Gauss-Newton (IC-GN) algorithm at each point of interest (POI). The experimental study shows that the developed method can deal with large and inhomogeneous deformation with high accuracy. Parallel computing (especially on GPU) accelerates significantly the proposed DIC method. The achieved computation speed satisfies the need for real-time processing with high resolution for the images of normal sizes.