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A registration error estimation framework for correlative imaging

Citation Author(s):
Guillaume Potier, Frédéric Lavancier, Stephan Kunne, Perrine Paul-Gilloteaux
Submitted by:
Guillaume POTIER
Last updated:
1 October 2021 - 7:47am
Document Type:
Poster
Document Year:
2021
Event:
Presenters Name:
Guillaume POTIER
Paper Code:
1563

Abstract 

Abstract: 

Correlative imaging workflows are now widely used in bio-imaging and aims to image the same sample using at least two different and complementary imaging modalities. Part of the workflow relies on finding the transformation linking a source image to a target image. We are specifically interested in the estimation of registration error in point-based registration. We propose an application of multivariate linear regression to solve the registration problem allowing us to propose a framework for the estimation of the associated error in the case of rigid and affine transformations and with anisotropic noise. These developments can be used as a decision-support tool for the biologist to analyze multimodal correlative images.

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