Adaptive partitioning method in high resolution speckle imagery for sub-pixel digital image correlation
Image processing methods have gained wide acceptance in the field of experimental mechanics for the measurement of full field displacements and strains in materials that undergo mechanical stress. Over the years various block based methods have been developed focusing on pixel and sub-pixel accurate displacement estimation, using as input data speckle images of the material before and after the deformation process. As high resolution imaging systems become more affordable, the limitations of block-based approaches become apparent because of their inherent inability to create high density motion fields while keeping the errors in the calculated motion vectors to a minimum due to the aperture problem. In this paper a novel low-level partitioning method is presented which adaptively divides the image into cells, according to the underlying speckle structure with the purpose of minimizing motion estimation errors. The method starts by determining relevant 'central' areas for speckles over a certain minimum size and subsequently builds the cells around these areas according to spatial distances and image structure. The results show clear improvements in motion accuracy compared to the regular block approach and provide a first step in the accurate isolation of various features in the images such as holes and cracks.