Publications

Digital Image Correlation typically involves deforming a pixelated image in order to compare its grey levels with those of another image. To achieve sub-pixel accuracy, grey-level interpolation is required. However, this interpolation is non-physical and introduces biases that become particularly detrimental under finite strains. In this work, we propose an alternative photometric approach that entirely avoids interpolation, grounded in a rigorous formulation of the direct image formation problem. The inverse problem is then posed as the joint estimation of a super-resolved digital twin—representing the scene and sensor characteristics—and the displacement fields. Both are estimated by minimising a single cost function that compares all available real images to their synthetic counterparts generated through a physically based rendering model. This minimisation is performed using an efficient alternating minimisation scheme. Several two-dimensional test cases are analysed, demonstrating that the proposed method is effectively unbiased and exhibits significantly lower uncertainties than state-of-the-art DIC techniques.

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Digital Image Correlation relies on matching two images taken at different instants, an approach built on several assumptions that can break down as strains grow large. We propose a new image-based measurement method that instead inverts the direct problem of pixel formation from the path of light. This measurement method is more accurate than digital image correlation, particularly for large strains.

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Measuring kinematic fields at the architecture scale of cellular materials is highly challenging in experimental mechanics, in particular because of the geometric complexity of the specimen and the poor texture at this scale. To overcome this obstacle, a solution consists in assisting Digital Volume Correlation (DVC) with a FE-based mechanical regularization. However, such approaches entail a substantial computational burden when considering complex architectured materials along with large volume images. To address this issue, this study proposes a scalable domain decomposition algorithm to efficiently estimate subcellular kinematic fields across large regions of cellular materials.

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Between 30 and 50% of breast and prostate cancer patients develop spinal metastases, which can lead to vertebral collapse and damage to the spinal cord. Our goal is to build a software tool that assesses this collapse risk by creating a personalized numerical avatar of a patient's vertebra, combining CT imaging with the bone's mechanical behaviour to predict its response through simulation. A key part of this project is Digital Volume Correlation (DVC): mapping a patient's CT scan onto a later follow-up scan and examining the residual difference between the two highlights the regions where a tumour has appeared, offering an automatic way to detect metastatic tumours in vertebrae. This contribution presents some of our first results with this approach, a promising step toward a clinical diagnostic aid tool, with automatic vertebra segmentation as the next development.

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