A robust feature-based full-field initial value estimation in path-independent digital image correlation for large deformation measurement

Abstract

The feature-based path-independent digital image correlation (DIC) method has been shown to be a formidable tool for non-contact, full-field measurement of large deformation, but its effectiveness hinges crucially on acquiring sufficient matched features to perform a reliable full-field initial value estimation (IVE) for all points of interest (POIs), thus ensuring their successful and rapid convergence in the succeeding path-independent iterative DIC refinement. This prerequisite is a challenging task particularly when confronted with large deformation. Moreover, in many real-world measurement scenarios, the accuracy of IVE is also influenced by image noise, such as Gaussian noise and shot noise, further compounding the challenge. To mitigate these issues, we propose a robust feature-based full-field IVE method. The core of this method consists of two main components: (i) For feature detection, we leverage the strengths of nonlinear multiscale representations on speckle images using an Accelerated-KAZE (A-KAZE) detector, which extracts features in a nonlinear scale space via nonlinear diffusion filtering. Noise is suppressed and edges are preserved. Compared to existing state-of-the-art feature detectors used in DIC, such as Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Feature (SURF) detectors, which rely on the use of Gaussian linear scale spaces, the A-KAZE-based nonlinear scale space detector identifies more salient features with higher localization accuracy. (ii) For feature description, considering the need for robustness against large deformation and the computational burden of descriptor matching for considerable salient features that may be detected in speckle images, we introduce a robust Gradient Location and Orientation Histogram (GLOH) descriptor and propose an improved version of it. The GLOH's improved version incorporates a restricted adaptive binning (RAB) strategy to optimize the descriptor’s structure parameters, which is able to reduce the computational cost of descriptor matching through restricting its dimensionality while without sacrificing its robustness and discriminability. These two components are designed to provide sufficient matched features for a full-field IVE. The initial deformation for each POI is estimated independently by fitting a local affine transformation model, which is refined to subpixel accuracy through iterative path-independent DIC analysis. To handle complex large deformation, the inverse compositional Gauss-Newton (IC-GN) algorithm with a second-order shape function is employed. Extensive experimental results demonstrate that our method has improved IVE accuracy as well as behaves more robustness against local geometric transformations and image noise including Gaussian noise and shot noise, as compared to existing state-of-the-art feature-based full-field IVE methods.