CNRS Research on Informatics and Random Systems
CNRS Research on Informatics and Random Systems
Fick R.H.J.,French Institute for Research in Computer Science and Automation |
Wassermann D.,French Institute for Research in Computer Science and Automation |
Caruyer E.,CNRS Research on Informatics and Random Systems |
Deriche R.,French Institute for Research in Computer Science and Automation
NeuroImage | Year: 2016
The recovery of microstructure-related features of the brain's white matter is a current challenge in diffusion MRI. To robustly estimate these important features from multi-shell diffusion MRI data, we propose to analytically regularize the coefficient estimation of the Mean Apparent Propagator (MAP)-MRI method using the norm of the Laplacian of the reconstructed signal. We first compare our approach, which we call MAPL, with competing, state-of-the-art functional basis approaches. We show that it outperforms the original MAP-MRI implementation and the recently proposed modified Spherical Polar Fourier (mSPF) basis with respect to signal fitting and reconstruction of the Ensemble Average Propagator (EAP) and Orientation Distribution Function (ODF) in noisy, sparsely sampled data of a physical phantom with reference gold standard data. Then, to reduce the variance of parameter estimation using multi-compartment tissue models, we propose to use MAPL's signal fitting and extrapolation as a preprocessing step. We study the effect of MAPL on the estimation of axon diameter using a simplified Axcaliber model and axonal dispersion using the Neurite Orientation Dispersion and Density Imaging (NODDI) model. We show the positive effect of using it as a preprocessing step in estimating and reducing the variances of these parameters in the Corpus Callosum of six different subjects of the MGH Human Connectome Project. Finally, we correlate the estimated axon diameter, dispersion and restricted volume fractions with Fractional Anisotropy (FA) and clearly show that changes in FA significantly correlate with changes in all estimated parameters.Overall, we illustrate the potential of using a well-regularized functional basis together with multi-compartment approaches to recover important microstructure tissue parameters with much less variability, thus contributing to the challenge of better understanding microstructure-related features of the brain's white matter. © 2016 Elsevier Inc.
Suarez R.O.,Harvard University |
Commowick O.,CNRS Research on Informatics and Random Systems |
Prabhu S.P.,Harvard University |
Warfield S.K.,Harvard University
NeuroImage | Year: 2012
White matter fiber bundles of the brain can be delineated by tractography utilizing multiple regions-of-interest (MROI) defined by anatomical landmarks. These MROI can be used to specify regions in which to seed, select, or reject tractography fibers. Manual identification of anatomical MROI enables the delineation of white matter fiber bundles, but requires considerable training to develop expertise, considerable time to carry out and suffers from unwanted inter- and intra-rater variability. In a study of 20 healthy volunteers, we compared three methodologies for automated delineation of the white matter fiber bundles. Using these methodologies, fiber bundle MROI for each volunteer were automatically generated. We assessed three strategies for inferring the automatic MROI utilizing nonrigid alignment of reference images and projection of template MROI. We assessed the bundle delineation error associated with alignment utilizing T1-weighted MRI, fractional anisotropy images, and full tensor images. We confirmed the smallest delineation error was achieved using the full tensor images. We then assessed three projection strategies for automatic determination of MROI in each volunteer. Quantitative comparisons were made using the root-mean-squared error observed between streamline density images constructed from fiber bundles identified automatically and by manually drawn MROI in the same subjects. We demonstrate that a multiple template consensus label fusion algorithm generated fiber bundles most consistent with the manual reference standard. © 2011 Elsevier Inc..
Liu Z.,Shanghai University |
Liu Z.,CNRS Research on Informatics and Random Systems |
Zou W.,Shenzhen University |
Zou W.,European University of Brittany |
Le Meur O.,University of Rennes 1
IEEE Transactions on Image Processing | Year: 2014
This paper proposes a novel saliency detection framework termed as saliency tree. For effective saliency measurement, the original image is first simplified using adaptive color quantization and region segmentation to partition the image into a set of primitive regions. Then, three measures, i.e., global contrast, spatial sparsity, and object prior are integrated with regional similarities to generate the initial regional saliency for each primitive region. Next, a saliency-directed region merging approach with dynamic scale control scheme is proposed to generate the saliency tree, in which each leaf node represents a primitive region and each non-leaf node represents a non-primitive region generated during the region merging process. Finally, by exploiting a regional center-surround scheme based node selection criterion, a systematic saliency tree analysis including salient node selection, regional saliency adjustment and selection is performed to obtain final regional saliency measures and to derive the high-quality pixel-wise saliency map. Extensive experimental results on five datasets with pixel-wise ground truths demonstrate that the proposed saliency tree model consistently outperforms the state-of-the-art saliency models. © 2014 IEEE.
Allibert G.,CNRS Informatics, Signals & Systems Lab in Sophia Antipolis |
Courtial E.,Institute Pluridisciplinaire Of Recherche En Ingenierie Des Systemes |
Chaumette F.,CNRS Research on Informatics and Random Systems
IEEE Transactions on Robotics | Year: 2010
This paper deals with the image-based visual servoing (IBVS), subject to constraints. Robot workspace limitations, visibility constraints, and actuators limitations are addressed. These constraints are formulated into state, output, and input constraints, respectively. Based on the predictive-control strategy, the IBVS task is written into a nonlinear optimization problem in the image plane, where the constraints can be easily and explicitly taken into account. Second, the contribution of the image prediction and influence of the prediction horizon are pointed out. The image prediction is obtained due to a model. The latter can be a local model based on the interaction matrix or a nonlinear global model based on 3-D data. Its choice is discussed with respect to the constraints to be handled. Finally, simulations that were obtained with a 6-degree-of-freedom (DOF) free-flying camera highlight the potential advantages of the proposed approach with respect to the image prediction and the constraint handling. © 2010 IEEE.
Gueguen C.,CNRS Research on Informatics and Random Systems |
Rachedi A.,University Paris Est Creteil |
Guizani M.,Qatar University
IEEE Transactions on Vehicular Technology | Year: 2013
In this paper, we focus on wireless coverage extension and nodes' cooperation. We propose a new protocol based on an incentive approach and a scheduling algorithm to reward cooperative nodes. The cost of cooperation can be prohibitively expensive in terms of quality of service (QoS) and energy consumption, which does not motivate some nodes to cooperate. Therefore, we introduce a percentage of cooperation and QoS parameters in the scheduling algorithm called coverage extension based on incentive scheduling to incite potential mobile relaying nodes to cooperate and, in turn, extend the wireless areas. We use the cross-layer approach to optimize the QoS parameters. The proposed solution not only incites the nodes to cooperate but enhances the QoS by increasing the average throughput and decreasing the delay as well. The simulation results show that the proposed solution not only gives better results than the well-known scheduling algorithms, such as maximum signal-to-noise ratio (MaxSNR) and weighted fair opportunistic (WFO), but allows the cooperative mobile nodes to increase their own throughput by around 114% as well. The total amount of data transmitted out of the cell to extend the coverage can be increased by around 59% compared with the scheduling algorithm MaxSNR. © 1967-2012 IEEE.
Ozerov A.,French Institute for Research in Computer Science and Automation |
Vincent E.,French Institute for Research in Computer Science and Automation |
Bimbot F.,CNRS Research on Informatics and Random Systems
IEEE Transactions on Audio, Speech and Language Processing | Year: 2012
Most audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the mixing process. In this paper, we introduce a general audio source separation framework based on a library of structured source models that enable the incorporation of prior knowledge about each source via user-specifiable constraints. While this framework generalizes several existing audio source separation methods, it also allows to imagine and implement new efficient methods that were not yet reported in the literature.We first introduce the framework by describing the model structure and constraints, explaining its generality, and summarizing its algorithmic implementation using a generalized expectation-maximization algorithm. Finally, we illustrate the above-mentioned capabilities of the framework by applying it in several new and existing configurations to different source separation problems. We have released a software tool named Flexible Audio Source Separation Toolbox (FASST) implementing a baseline version of the framework in Matlab. © 2011 IEEE.
Nguyen T.-D.,Vietnam National University, Ho Chi Minh City |
Berder O.,CNRS Research on Informatics and Random Systems |
Sentieys O.,CNRS Research on Informatics and Random Systems
IEEE Transactions on Intelligent Transportation Systems | Year: 2011
In wireless distributed networks, cooperative relay and cooperative multiple-input-multiple-output (MIMO) techniques can be used to exploit the spatial and temporal diversity gains to increase the performance or reduce the transmission energy consumption. The energy efficiency of cooperative MIMO and relay techniques is then very useful for the infrastructure-to-vehicle (I2V) and infrastructure-to-infrastructure (I2I) communications in intelligent transport system (ITS) networks, where the energy consumption of wireless nodes embedded on road infrastructure is constrained. In this paper, applications of cooperation between nodes to ITS networks are proposed, and the performance and the energy consumption of cooperative relay and cooperative MIMO are investigated and compared with the traditional multihop technique. The comparison between these cooperative techniques helps us choose the optimal cooperative strategy in terms of energy consumption for energy-constrained road infrastructure networks in ITS applications. © 2006 IEEE.
Le Meur O.,CNRS Research on Informatics and Random Systems |
Ebdelli M.,French Institute for Research in Computer Science and Automation |
Guillemot C.,French Institute for Research in Computer Science and Automation
IEEE Transactions on Image Processing | Year: 2013
This paper introduces a novel framework for examplar-based inpainting. It consists in performing first the inpainting on a coarse version of the input image. A hierarchical super-resolution algorithm is then used to recover details on the missing areas. The advantage of this approach is that it is easier to inpaint low-resolution pictures than high-resolution ones. The gain is both in terms of computational complexity and visual quality. However, to be less sensitive to the parameter setting of the inpainting method, the low-resolution input picture is inpainted several times with different configurations. Results are efficiently combined with a loopy belief propagation and details are recovered by a single-image super-resolution algorithm. Experimental results in a context of image editing and texture synthesis demonstrate the effectiveness of the proposed method. Results are compared to five state-of-the-art inpainting methods. © 1992-2012 IEEE.
Dame A.,French Institute for Research in Computer Science and Automation |
Marchand E.,CNRS Research on Informatics and Random Systems
Proceedings - IEEE International Conference on Robotics and Automation | Year: 2011
In this paper we propose a new way to achieve a navigation task for a non-holonomic vehicle. We consider an image-based navigation process. We show that it is possible to navigate along a visual path without relying on the extraction, matching and tracking of geometric visual features such as keypoint. The new proposed approach relies directly on the information (entropy) contained in the image signal. We show that it is possible to build a control law directly from the maximisation of the shared information between the current image and the next key image in the visual path. The shared information between those two images are obtained using mutual information that is known to be robust to illumination variations and occlusions. Moreover the generally complex task of features extraction and matching is avoided. Both simulations and experiments on a real vehicle are presented and show the possibilities and advantages offered by the proposed method. © 2011 IEEE.
Dame A.,University of Oxford |
Marchand E.,CNRS Research on Informatics and Random Systems
IEEE Transactions on Image Processing | Year: 2012
In this paper, we present a direct image registration approach that uses mutual information (MI) as a metric for alignment. The proposed approach is robust and gives an accurate estimation of a set of 2-D motion parameters in real time. MI is a measure of the quantity of information shared by signals. Although it has the ability to perform robust alignment with illumination changes, multimodality, and partial occlusions, few works have proposed MI-based applications related to spatiotemporal image registration or object tracking in image sequences because of some optimization problems, which we will explain. In this paper, we propose a new optimization method that is adapted to the MI cost function and gives a practical solution for real-time tracking. We show that by refining the computation of the Hessian matrix and using a specific optimization approach, the registration results are far more robust and accurate than the existing solutions, with the computation also being cheaper. A new approach is also proposed to speed up the computation of the derivatives and keep the same optimization efficiency. To validate the advantages of the proposed approach, several experiments are performed. © 1992-2012 IEEE.