Jean-Philippe Thiran

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Visual speech recognition is a challenging research problem with a particular practical application of aiding audio speech recognition in noisy scenarios. Multiple camera setups can be beneficial for the visual speech recognition systems in terms of improv ...

It has been shown that the tensor calculation is very sensitive to the presence of noise in the acquired images, yielding to very low-quality Diffusion Tensor Images (DTI) data. Recent investigations have shown that the noise present in the Diffusion Weigh ...

Combining several classifiers has proved to be an efficient machine learning technique. Two concepts influence clearly the efficiency of an ensemble: the diversity between classifiers and the individual accuracies of the classifiers. We use an information theoretic framework to establish a link between these quantities and as they appear to be contradictory, we propose an information theoretic measure that express a trade-off between individual accuracy and diversity. This technique can be directly adapted

The current diagnostic criteria for multiple sclerosis (MS) lack specificity, and this may lead to misdiagnosis, which remains an issue in present-day clinical practice. In addition, conventional biomarkers only moderately correlate with MS disease progres ...

In this paper we introduce the Incremental Temporally Weighted Principal Component Analysis (ITWPCA) algorithm, based on SVD update, and the Incremental Temporally Weighted Visual Tracking with Spatial Penalty (ITWVTSP) algorithm for robust visual tracking. ITWVTSP uses ITWPCA for computing incrementally a robust low dimensional subspace representation (model) of the tracked object. The robustness is based on the capacity of weighting the contribution of each single sample to the subspace generation, in ord

In this paper, a fast and an effective multi-view face tracking algorithm with head pose estimation is introduced. For modeling the face pose we employ a tree of boosted classiﬁers built using either Haar-like ﬁlters or Gauss ﬁlters. A ﬁrst classiﬁer extra ...

Wearable devices based on photoplethysmography (PPG) allow for the screening of large populations at risk of cardiovascular disease. While PPG has shown the ability to discriminate atrial fibrillation (AF)-the most common cardiac arrhythmia (CA)-versus nor ...

Objective: Home-based non-invasive brain stimulation (NIBS) has been suggested as an adjunct treatment strategy for neuro-psychiatric disorders. There are currently no available solutions to direct and monitor correct placement of the stimulation electrode ...

Using a low-level representation of images, like matching pursuit, we introduce a new way of describing objects through a general description using a translation, rotation, and isotropic scale invariant dictionary of basis functions. We then use this description as a predefined dictionary of the object to conduct a shape recognition task. We show some promising results for the detection with simple shapes.

This paper proposes a solution to perform active debris removal with a cost effective microsatellite. A complex aspect of debris removal in space is the detection and positive identification of the debris, medium to close approach as well as the orbital rendezvous and following on-site operations. These aspects will require a mix of several technologies, some of which already exist, and some of which will need to be miniaturized and adapted for programs such as CleanSpace One. The rendezvous phases in parti

Flow is a mental state experienced during holistic involvement in a certain task, and it is a factor that promotes motivation, development, and performance. A reliable and objective estimation of the flow is essential for moving away from the traditional s ...

One main goal of Diffusion-Weighted Magnetic Resonance Imaging is to infer microscopic tissue properties from the measured signal. The use of Monte-Carlo Simulations for DW-MRI on realistic substrates will help the study of DWI-MRI signals in controlled environments and to investigate, extract, and validate approaches for the understanding of white matter features. In this work, we present a novel framework for creating complex white matter phantoms with unprecedented characteristics like high packing densi

Training Support Vector Machine can become very challenging in large scale problems. Training several lower complexity SVMs on local subsets of the training set can significantly reduce the training complexity and also improve the classification performances. In order to obtain efficient multiple classifiers systems, classifiers need to be both diverse and individually accurate. In this paper we propose an algorithm for training ensembles of SVMs by taking into account diversity between each parallel classi

Object. The authors describe a new method of localizing electrodes on magnetic resonance (MR) images and focus on the positions of both the most efficient contact and the electrode related to the MR imaging target. Methods. Thirty-one patients who had unde ...

Audio-visual speech recognition promises to improve the performance of speech recognizers, especially when the audio is corrupted, by adding information from the visual modality, more specifically, from the video of the speaker. However, the number of visu ...

In this paper we describe a new approach for the multitrack initiation problem. We propose an extensive use of the top-view reconstruction of the scene to solve the detection step in tracking pedestrians. We leave a large set of starting hypothetical moving objects free to evolve in the scene for a certain number of frames. The number of trajectories is pre-filtered using distance and direction constraints on a one-step movement of pedestrians. The output trajectories from pre-filtering step are then filter

In this paper a new extension of the CONDENSATION algorithm, with application to infants face tracking, will be introduced. In this work we address the problem of tracking a face and its features in baby video sequences. A mixed state particle filtering sc ...

Based on the success of deep neural networks for image recovery, we propose a new paradigm for the compression and decompression of ultrasound~(US) signals which relies on stacked denoising autoencoders. The first layer of the network is used to compress the signals and the remaining layers perform the reconstruction. We train the network on simulated US signals and evaluate its quality on images of the publicly available PICMUS dataset. We demonstrate that such a simple architecture outperforms state-of-th

In this paper we present a new methodology to extract the small error-prone motion recorded during a Computed Tomography (CT) imaging directly from the sinogram. In comparison to the use of the usual reconstructed images, this approach has the advantage to minimize the reconstruction errors and artifacts. Moreover, the B-spline modeling of the motion has brought about the required accuracy for the detection of such a small motion. The windowing used can act as a kind of time regularization, in case of volum

The level set method is a popular technique for tracking moving interfaces in several disciplines including computer vision and fluid dynamics. However, despite its high flexibility, the original level set method is limited by two important numerical issue ...

Jean-Philippe Thiran

Chattez avec Graph Search

Combining Multiple Views for Visual Speech Recognition

Sequential Anisotropic Multichannel Wiener Filtering with Rician Bias Correction Applied to 3D Regularization of DWI Data

Information Theoretic Combination of Classifiers With Application to Multiple SVMs

Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: Emerging machine learning techniques and future avenues

Sample and Pixel Weighting Strategies for Robust Incremental Visual Tracking

Fast multi-view face tracking with pose estimation

Abnormal Cardiac Rhythm Detection Based on Photoplethysmography Signals and a Recurrent Neural Network

Computer vision-based algorithm to sUppoRt coRrect electrode placemeNT (CURRENT) for home-based electric non-invasive brain stimulation

Rotation and scale invariant shape representation and recognition using Matching Pursuit

Uncooperative Rendezvous and Docking for MicroSats

Mental Flow Estimation Through Wearable EEG

Towards a high-density packing white matter substrate generator for Monte-Carlo simulations

Ensembles of SVMs using an Information Theoretic Criterion

Localization of electrodes in the subthalamic nucleus on magnetic resonance imaging

Low-Dimensional Motion Features for Audio-Visual Speech Recognition

BEHAVIORAL FILTERING OF HUMAN TRAJECTORIES FOR AUTOMATIC-MULTI-TRACK INITIATION

Robust infants face tracking using active appearance models: a mixed-state CONDENSATION approach

A Deep Learning Approach to Ultrasound Image Recovery

Direct Detection of Small Motion from Computed Tomography Images

An Efficient Algorithm for Level Set Method Preserving Distance Function