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Automated Fast Initial Guess in Digital Image Correlation
Author(s) -
Wang Z.,
Vo M.,
Kieu H.,
Pan T.
Publication year - 2014
Publication title -
strain
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.477
H-Index - 47
eISSN - 1475-1305
pISSN - 0039-2103
DOI - 10.1111/str.12063
Subject(s) - scale invariant feature transform , rotation (mathematics) , digital image correlation , artificial intelligence , matching (statistics) , invariant (physics) , image matching , algorithm , computer science , feature matching , digital image , sample (material) , pattern recognition (psychology) , feature (linguistics) , blossom algorithm , scale (ratio) , computer vision , scale invariance , image processing , mathematics , image (mathematics) , statistics , linguistics , physics , chemistry , philosophy , chromatography , quantum mechanics , optics , mathematical physics
A challenging task that has hampered the fully automatic processing of the digital image correlation (DIC) technique is the initial guess when large deformation and rotation are present. In this paper, a robust scheme combining the concepts of a scale‐invariant feature transform (SIFT) algorithm and an improved random sample consensus (iRANSAC) algorithm is employed to conduct an automated fast initial guess for the DIC technique. The scale‐invariant feature transform algorithm can detect a certain number of matching points from two images even though the corresponding deformation and rotation are large or the images have periodic and identical patterns. After removing the wrong matches with the improved random sample consensus algorithm, the three pairs of closest and non‐collinear matching points serve for the purpose of initial guess calculation. The validity of the technique is demonstrated by both computer simulation and real experiment.