Publications

Modeling and Simulation

Exact Sampling of Determinantal Point Processes without Eigendecomposition

Determinantal point processes (DPPs) enable the modeling of repulsion: they provide diverse sets of points. The repulsion is encoded in a kernel K that can be seen as a matrix storing the similarity between points. The diversity comes from the fact…

Claire Launay, Bruno Galerne, Agnès Desolneux

Journal of Applied Probability - 2020

Mathematics

Redundancy in Gaussian random fields

In this paper, we introduce a notion of spatial redundancy in Gaussian random fields. This study is motivated by applications of the a contrario method in image processing. We define similarity functions on local windows in random fields over…

Valentin de Bortoli, Agnès Desolneux, Bruno Galerne, Arthur Leclaire

ESAIM: Probability and Statistics - 2020

Biomechanics

Center of pressure displacement characteristics differentiate fall risk in older people: A systematic review with meta-analysis

Falling is the second most prevalent cause of accidental death in the world. Currently available clinical tests to assess balance in older people are insufficiently sensitive to screen for fall risk in this population. Laboratory tests that record…

Flavien Quijoux, Aliénor Vienne-Jumeau, François Bertin-Hugault, Philippe Zawieja, Pierre-Paul Vidal, Damien Ricard

Ageing Research Reviews - ARR - 2020

Signal and Image processing

Local JPEG Grid Detector via Blocking Artifacts, a Forgery Detection Tool

Tina Nikoukhah, Miguel Colom, Jean-Michel Morel, Rafael Grompone von Gioi

Image Processing On Line - 2020

Probability

Determinantal point processes for image processing

Determinantal point processes (DPPs) are probabilistic models of configurations that favour diversity or repulsion. They have recently gained influence in the machine learning community, mainly because of their ability to elegantly and efficiently…

Claire Launay, Agnès Desolneux, Bruno Galerne

SIAM Journal on Imaging Sciences - 2020

Image Processing

Depth Map Quality Evaluation for Photographic Applications

As depth imaging is integrated into more and more consumer devices, manufacturers have to tackle new challenges. Applications such as computational bokeh and augmented reality require dense and precisely segmented depth maps to achieve good results…

Eloi Zalczer, François-Xavier Thomas, Laurent Chanas, Gabriele Facciolo, Frédéric Guichard

Electronic Imaging - 2020

Signal and Image processing

Low Rank Activations for Tensor-Based Convolutional Sparse Coding

Pierre Humbert, Julien Audiffren, Laurent Oudre, Nicolas Vayatis

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - 2020

Engineering Sciences

Personalized Template-Based Step Detection From Inertial Measurement Units Signals in Multiple Sclerosis

Aliénor Vienne-Jumeau, Laurent Oudre, Albane Moreau, Flavien Quijoux, Sébastien Edmond, Mélanie Dandrieux, Eva Legendre, Pierre Paul Vidal, Damien Ricard

Frontiers in Neurology - 2020

Engineering Sciences

Non-Linear Template-Based Approach for the Study of Locomotion

Tristan Dot, Flavien Quijoux, Laurent Oudre, Aliénor Vienne-Jumeau, Albane Moreau, Pierre-Paul Vidal, Damien Ricard

Sensors - 2020

Signal and Image processing

A Review of an Old Dilemma: Demosaicking First, or Denoising First?

Image denoising and demosaicking are the most important early stages in digital camera pipelines. They constitute a severely ill-posed problem that aims at reconstructing a full color image from a noisy color filter array (CFA) image. In most of the…

Qiyu Jin, Gabriele Facciolo, Jean-Michel Morel

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) - 2020