Jean-Philippe Thiran

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Data collected through a recent web-based survey show that the perception (i.e. labeling) of a human facial expression by a human observer is a subjective process, which results in a lack of a unique ground-truth, as intended in the standard classification ...

This paper describes a new approach to automatic frontal face detection which employs Gaussian filters as local image descriptors. We then show how the paradigm of classifier combination can be used for building a face detector that outperforms the current ...

Evidence from activation studies suggests that sound recognition and localization are processed in two distinct cortical networks that are each present in both hemispheres. Sound recognition and/or localization may, however, be disrupted by purely unilateral damage, suggesting that processing within one hemisphere may not be sufficient or may be disturbed by the contralateral lesion. Sound recognition and localization were investigated psychophysically and using fMRI in patients with unilateral right hemisp

Normal aging and a wide range of neurologic, inflammatory or psychiatric diseases lead to changes in the brain tissue over time. In the interest of diagnosis, prognosis and treatment monitoring, it is highly desirable to have robust tools that reliably measure brain morphometry. We explore the ability of an automated MR image quality assessment technique to predict the accuracy of subsequent algorithms for brain quantitative analysis. The approach proofs to be a very promising candidate to objectively asses

Given the high prevalence of aging-associated cerebral small vessel disease in the general population, accurate detection of the related white matter hyperintensities (WMH) in large-scale magnetic resonance imaging (MRI) studies is of critical importance. The performance of currently available semi-automated and automated methods for WMH classification is hampered by their inherent dependence on MRI contrast parameters and long computational processing time. We sought to improve the accuracy and computation

We present a method for multimodal fusion based on the estimated reliability of each individual modality. Our method uses an information theoretic measure, the entropy derived from the state probability distribution for each stream, as an estimate of reliability. Our application is audio-visual speech recognition. The two modalities, audio and video, are weighted at each time instant according to their reliability. In this way, the weights vary dynamically and are able to adapt to any type of noise in each

Recently, the R2D2 paradigm, standing for ‘Residual-to-Residual DNN series for high-Dynamic-range imaging’, was introduced for image formation in Radio Interferometry (RI) as a learned version of the traditional algorithm CLEAN. The first incarnations of R ...

We propose a fully three-dimensional object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, w

In this paper the problem of intensity inhomogeneity at high magnetic field on magnetic resonance images is addressed. Specifically, rat brain images at 9.4T acquired with a surface coil are bias corrected. We propose a low- pass frequency model that takes ...

In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during th ...

Tracking vocal tract formant frequencies (

f_p

) and estimating the fundamental frequency (

f_0

) are two tracking problems that have been tackled in many speech processing works, often independently, with applications to articulatory parameters estimation ...

We consider the problem of testing positively dependent multiple hypotheses assuming that a prior information about the dependence structure is available. We propose two-step multiple comparisons procedures that exploit the prior information of the dependence structure, without relying on strong assumptions. In the first step, we group the tests into subsets where tests are supposed to be positively dependent and in each of which we compute the standardized mean of the test scores. Given the subset mean sco

Motivation: The infeasibility to collect large datasets of high-resolution, fully-sampled 7T data hinders the training of deep learning reconstructions for accelerated high-resolution 7T scans. Goal(s): Demonstrate that combining pretraining on fully-sampled scans from 1.5/3T and self-supervised finetuning on a few undersampled 7T scans enables robust reconstruction. Approach: High-resolution(0.5mm isotropic), 3D, 7T MP2RAGE scans of multiple sclerosis patients are used for finetuning/testing. Results: Deep

This paper presents a validation study on statistical non-supervised brain tissue classification techniques in MR images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of cla ...

The autocorrelations have been previously used as features for 1D or 2D signal classification in a wide range of applications, like texture classification, face detection and recognition, EEG signal classification, and so on. However, in almost all the cases, the high computational costs have hampered the extension to higher orders (more than the second order). In this paper we present an effective method for using higher order autocorrelation functions for pattern recognition. We will show that while the a

A SIFT algorithm in spherical coordinates for omnidirectional images is proposed. This algorithm can generate two types of local descriptors, Local Spherical Descriptors and Local Planar Descriptors. With the first ones, point matching between two omnidire ...

Objects. In functional stereotactic neurosurgery, one of the cornerstones upon which the success and the operating time depends is an accurate targeting. The subthalamic nucleus (STN) is the usual target involved when applying Deep Brain Stimulation (DBS) ...

Aquaporins provide a unique approach for imaging genetic activity in deep tissues by increasing the rate of cellular water diffusion, which generates a magnetic resonance contrast. However, distinguishing aquaporin signals from the tissue background is cha ...

Magnetic Resonance Imaging (MRI) is nowadays a widely used medical tool, as it is a non-invasive and non-harmful way to study inner soft tissues. One of the characteristics of this method is the bias field, also called Intensity In-homogeneity (IIH), which is an artifact that affects quantitative image analysis consisting in a low frequency variation of the brightness through all the image acquired. This undesired effect makes difficult medical functions such as visual inspection or also intensity-based seg

A sound that we hear in a natural setting allows us to identify the sound source and localize it in space. The two aspects can be disrupted independently as shown in a study of 15 patients with focal right-hemispheric lesions. Four patients were normal in sound recognition but severely impaired in sound localization, whereas three other patients had difficulties in recognizing sounds but localized them well. The lesions involved the inferior parietal and frontal cortices, and the superior temporal gyrus in

Jean-Philippe Thiran

Chattez avec Graph Search

Modelling human perception of static facial expressions

Mixtures of Boosted Classifiers for Frontal Face Detection

Unilateral hemispheric lesions disrupt parallel processing within the contralateral intact hemisphere: an auditory fMRI study

Automated quality control in MR-based brain morphometry

WHITE-Net : White matter HyperIntensities Tissue Extraction using deep learning Network

Audio-visual reliability estimates using stream entropy for speech recognition

S-R2D2: a spherical extension of the R2D2 deep neural network series paradigm for wide-field radio-interferometric imaging

Lossy to lossless object-based coding of 3-D MRI data

Fast Bias Field Correction for 9.4 Tesla Magnetic Resonance Imaging

Crop Backscatter Modeling And Soil Moisture Estimation With Support Vector Regression

Source/Filter Factorial Hidden Markov Model, with Application to Pitch and Formant Tracking

Two step multiple comparisons procedures for positively dependent data with application to detecting differences in human brain network topologies

Supervised Pretraining and Self-Supervised Finetuning enables Robust Reconstruction of High-Resolution 7T MP2RAGE for Multiple Sclerosis

Comparison and Validation of Tissue Modelization and Statistical Classification Methods in T1-weighted MR Brain Images

Pattern Recognition using Higher-Order Local Autocorrelation Coefficients

Scale Invariant Feature Transform on the Sphere: Theory and Applications

Validation of Experts Versus Atlas-Based and Automatic Registration Methods for Subthalamic Nucleus Targeting on MRI

Molecular Imaging with Aquaporin-Based Reporter Genes: Quantitative Considerations from Monte Carlo Diffusion Simulations

Bias Field Correction in Magnetic Resonance Images of a Rat Brain

What and where in human audition: selective deficits following focal hemispheric lesions