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Adrian Basarab
Researcher at University of Toulouse
Publications - 174
Citations - 2664
Adrian Basarab is an academic researcher from University of Toulouse. The author has contributed to research in topics: Motion estimation & Deconvolution. The author has an hindex of 26, co-authored 159 publications receiving 2125 citations. Previous affiliations of Adrian Basarab include Paul Sabatier University & Centre national de la recherche scientifique.
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Journal ArticleDOI
A New Technique for the Estimation of Cardiac Motion in Echocardiography Based on Transverse Oscillations: A Preliminary Evaluation In Silico and a Feasibility Demonstration In Vivo
Martino Alessandrini,Adrian Basarab,Loic Boussel,Xinxin Guo,André Sérusclat,Denis Friboulet,Denis Kouame,Olivier Bernard,Herve Liebgott +8 more
TL;DR: A novel setup for the estimation of cardiac motion with ultrasound is introduced including an unconventional beamforming technique and a dedicated motion estimation algorithm that directly exploits the phase information in the two directions by decomposing the image into two 2-D single-orthant analytic signals.
Journal ArticleDOI
Analytic Estimation of Subsample Spatial Shift Using the Phases of Multidimensional Analytic Signals
TL;DR: In this paper, a method of analytic subsample spatial shift estimation based on an a priori n-D signal model is proposed, which uses the linear phases of n analytic signals defined with the multidimensional Hilbert transform.
Journal ArticleDOI
Using Smart Offices to Predict Occupational Stress
TL;DR: This work contributes significantly towards the development of an unobtrusive and ubiquitous early stress detection system in smart office environments, whose implementation in the industrial environment would make a great beneficial impact on workers’ health status and on the economy of companies.
Journal ArticleDOI
Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning
TL;DR: The results show that the proposed method gives competitive results for the considered data, and the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors.
Proceedings ArticleDOI
Regularized Bayesian compressed sensing in ultrasound imaging
TL;DR: A new Bayesian model based on a correlated Bernoulli Gaussian model is proposed for ultrasound imaging and the parameters of this model can be estimated by sampling the corresponding posterior distribution using an MCMC method.