In this paper, we address the problem of approximating the atoms of a parametric dictionary A = {a(θ) : θ ∈ Θ}, commonly encountered in the context of sparse representations in "continuous" dictionaries. We focus on the case of translation-invariant dictionaries, where the inner product between a(θ) and a(θ) only depends on the difference θ − θ. We...
Published in IEEE transactions on pattern analysis and machine intelligence
In this article we study the adaptation of the concept of homography to Rolling Shutter (RS) images. This extension has never been clearly adressed despite the many roles played by the homography matrix in multi-view geometry. We first show that a direct point-to-point relationship on a RS pair can be expressed as a set of 3 to 8 atomic 3x3 matrice...
Hierarchical clustering (HC) is a powerful tool in data analysis since it allows discovering patterns in the observed data at different scales. Similarity-based HC methods take as input a fixed number of points and the matrix of pairwise similarities and output the dendrogram representing the nested partition. However, in some cases, the entire dat...
The quality of ultrasound images of welds is highly penalized by the dendritic structure of the material that forms during cooling. The image of a flaw is all the more degraded as a reliable description of such a medium is most of the time not possible, due to the poor knowledge on the weld at time of inspection. In a previous work, we demonstrated...
Incremental Expectation Maximization (EM) algorithms were introduced to design EM for the large scale learning framework by avoiding the full data set to be processed at each iteration. Nevertheless, these algorithms all assume that the conditional expectations of the sufficient statistics are explicit. In this paper, we propose a novel algorithm n...
We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MPEG G-PCC codec. Our previous work based on autoregressive models (VoxelDNN [1]) has a fast training phase, however, inference is slow as the occupancy probabilities are p...
This paper addresses physics-informed deep learning schemes for satellite ocean remote sensing data. Such observation datasets are characterized by the irregular space-time sampling of the ocean surface due to sensors' characteristics and satellite orbits. With a focus on satellite altimetry, we show that end-to-end learning schemes based on variat...
Published in Frontiers in Neuroscience
Purpose: Since their first generation in 2013, the use of cerebral organoids has spread exponentially. Today, the amount of generated data is becoming challenging to analyze manually. This review aims to overview the current image acquisition methods and to subsequently identify the needs in image analysis tools for cerebral organoids. Methods: To ...
Point cloud (PC) quality assessment is of fundamental importance to enable the efficient processing, coding and transmission of 3D data for virtual/augmented reality, autonomous driving, cultural heritage, etc. The quality metrics proposed so far aim at quantifying the distortion in the PC geometry and/or attributes with respect to a reference pris...
In this paper, we propose a new algorithm to estimate all the parameters of a Self Exited Threshold AutoRegressive (SETAR) model from an observed time series. The aim of this algorithm is to relax all the hypotheses concerning the SETAR model for instance, the knowledge (or assumption) of the number of regimes, the switching variables, as well as o...
