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Feature‐assisted stereo correlation
Author(s) -
Thiruselvam N. Iniyan,
Subramanian Sankara J.
Publication year - 2019
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.12315
Subject(s) - scale invariant feature transform , perspective (graphical) , feature (linguistics) , artificial intelligence , ground truth , matching (statistics) , correlation , computer science , digital image correlation , computer vision , convergence (economics) , stereopsis , mathematics , image (mathematics) , pattern recognition (psychology) , algorithm , geometry , statistics , optics , philosophy , linguistics , physics , economics , economic growth
Stereo correlation in digital image correlation (DIC) involves an optimisation problem that is sensitive to initial guess. In practice, this problem is circumvented by manually selecting a pair of points in the two stereo images that guarantees convergence and provides stereo mapping parameter estimates that are used as initial guesses at neighbouring subsets. However, such an approach is not always feasible, especially in the presence of substantial perspective distortions, for example, due to large stereo angles or complexities in specimen geometry. Therefore, it is desirable to provide high‐quality independent initial estimates over the entire region of interest. Recently, SIFT has been used for this purpose, but it fails when perspective distortions are severe. In this work, we investigate seven other feature‐based matching techniques to address this gap. Among these, DeepFlow algorithm provides the highest quality and most spatially uniform initial estimates. Further, we use DeepFlow estimates as initial guesses in a conventional stereo optimisation to compute geometry measures of a specimen in DIC challenge dataset. These geometry measures show excellent agreement with ground truth, further supporting the choice of DeepFlow in stereo correlation.