Thibaud Ehret

I am currently a post-doc at the Centre Borelli at ENS Paris-Saclay, where I work on anomaly detection and different projects linked with remote sensing.

I did my PhD at the CMLA at ENS Cachan, where I was advised by Jean-Michel Morel.

Email  /  GitHub  /  Google Scholar

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Research

I'm interested in image and video processing, optimization and remote sensing.

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Structure Tensor Representation for Robust Oriented Object Detection


Xavier Bou, Gabriele Facciolo, Rafael Grompone Von Gioi, Jean-Michel Morel, Thibaud Ehret
Preprint, 2024
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We propose to represent orientation as a structure tensor in Oriented Object Detection, bridging the gap between Gaussian-based and angle-coder solutions.
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Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery


Xavier Bou, Gabriele Facciolo, Rafael Grompone Von Gioi, Jean-Michel Morel, Thibaud Ehret
CVPR Workshops, 2024
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We explore recent ideas on Open Vocabulary Detection to detect any object in remote sensing images with only a handful of examples.
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Portraying the Need for Temporal Data in Flood Detection via Sentinel-1


Xavier Bou, Thibaud Ehret, Rafael Grompone von Gioi, Jérémy Anger
IGARSS, 2024
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We address current limitations in flood detection and illustrate the importance of temporal information to solve the flood detection problem, extending the MMFlood dataset to multi-date.
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Statistical modeling of deep features reduces false alarms in video change detection


Xavier Bou, Aitor Artola, Thibaud Ehret, Gabriele Facciolo, Jean-Michel Morel, Rafael Grompone von Gioi
Preprint, 2024
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Introduces a weakly supervised a-contrario validation process, based on high dimensional statistical modeling of deep features, to reduce the number of false alarms of any change detection algorithm.
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Reviewing ViBe, a Popular Background Subtraction Algorithm for Real-Time Applications


Xavier Bou, Thibaud Ehret, Gabriele Facciolo, Jean-Michel Morel, Rafael Grompone von Gioi
IPOL, 2022
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We review the classical video background subtraction algorithm ViBe, which is still widely due to its simplicity and low computational load. An interactive demo is provided to quickly run and visualize its results via an easy-to-use interface.
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A Study of RobustNet, a Domain Generalization Method for Semantic Segmentation


Xavier Bou
IPOL, 2022
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A review of RobustNet, a Domain Generalization method for Urban-Scene Semantic Segmentation. Instead of exposing the network to a wide range of domains, RobustNet tries to separate domain-variant from domain-invariant features via a whitening transformation. A demo is provided to easily test RobustNet and see the results on your own data.




Design and source code from Jon Barron's website and Leonid Keselman's website