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Thomas Corpetti
Researcher at University of Rennes
Publications - 143
Citations - 2445
Thomas Corpetti is an academic researcher from University of Rennes. The author has contributed to research in topics: Motion estimation & Change detection. The author has an hindex of 22, co-authored 133 publications receiving 2051 citations. Previous affiliations of Thomas Corpetti include University of Rennes 1 & Chinese Academy of Sciences.
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Dense estimation of fluid flows
TL;DR: A dedicated minimization-based motion estimator based on an integrated version of the continuity equation of fluid mechanics, which is compatible with large displacements and associated with an original second-order div-curl regularization.
Journal Article
Fluid experimental flow estimation based on an optical-flow scheme. Experiments in fluids
TL;DR: In this paper, an image-based integrated version of the continuity equation is proposed to provide accurate dense motion fields, which preserve divergence and vorticity blobs of the motion field.
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Fluid experimental flow estimation based on an Optical-flow scheme
TL;DR: The proposed technique is an extension of “optical-flow” schemes used in the computer vision community, which includes a specific enhancement for fluid mechanics applications, and enables to provide accurate dense motion fields.
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Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree
TL;DR: This work proposes to exploit the benefit of Sentinel-2 images to monitor urban areas and to update Copernicus Land services, in particular the High Resolution Layer imperviousness, using independent image classification and data fusion that are fused using the Dempster–Shafer theory.
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Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring
TL;DR: The results show that the classification accuracy of SAR variables is higher than those using optical data, and highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy.