We present ACCD, an interaction technique that allows direct drawing of long curves on 3D shapes with a tablet display over both multiple depth layers and multiple viewpoints. ACCD reduces the number of explicit viewpoint manipulations by combining self-occlusion management and automated camera control. As such it enables drawing on occluded faces but also around a 3D shape while keeping a constant drawing precision. Our experimental results indicates the efficacy of ACCD over conventional techniques.

Standard detection of evoked brain activity in functional MRI (fMRI) relies on a fixed and known shape of the impulse response of the neurovascular coupling, namely the hemo-dynamic response function (HRF). To cope with this issue, the joint detection-estimation (JDE) framework has been proposed. This formalism enables to estimate a HRF per region but for doing so, it assumes a prior brain partition (or parcellation) regarding hemodynamic territories. This partition has to be accurate enough to recover accurate HRF shapes but has also to overcome the detection-estimation issue: the lack of hemodynamics information in the non-active positions. An hemodynamically-based parcellation method is proposed, consisting first of a feature extraction step, followed by a Gaussian Mixture-based parcellation, which considers the injection of the activation levels in the parcellation process, in order to overcome the detection-estimation issue and find the underlying hemodynamics.

In many neuroscience applications, the Arterial Spin Labeling (ASL) fMRI modality arises as a preferable choice to the standard BOLD modality due to its ability to provide a quantitative measure of the Cerebral Blood Flow (CBF). Such a quantification is central but generally performed without consideration of a specific modeling of the perfusion component in the signal often handled via standard GLM approaches using the BOLD canonical response function as regressor. In this work, we propose a novel Bayesian hierarchical model of the ASL signal which allows activation detection and both the extraction of a perfusion and a hemodynamic component. Validation on synthetic and real data sets from event-related ASL show the ability of our model to address the source separation and double deconvolution problems inherent to ASL data analysis.

This paper presents a study of a method for estimating the position and orientation of a photo-shoot in indoor environments for augmented reality applications. Our proposed localization method is based on registered frame data of virtualized reality models, which are photos with known photo-shoot positions and orientations, and depth data. Because registered frame data are secondary product of modeling process, additional works are not necessary to create registered frame data especially for the localization. In the method, a photo taken by a mobile camera is compared to registered frame data for the localization. Since registered frame data are linked with photo-shoot position, orientation, and depth data, 3D coordinates of each pixel on the photo of registered frame data is available. We conducted experiments with employing five techniques of the estimation for comparative evaluations.

In standard within-subject analyses of event-related functional magnetic resonance imaging (fMRI) data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based joint detection-estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian model. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to sample the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a variational expectation-maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows automatic fine-tuning of spatial regularization parameters. It provides a new algorithm that exhibits interesting properties in terms of estimation error and computational cost compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model misspecification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery.

We address the issue of jointly detecting brain activity and estimating brain hemodynamics from functional MRI data. To this end, we adopt the so-called Joint-Detection-Estimation (JDE) framework introduced in [1] and augmented in [2]. An inherent difficulty is to find the right spatial scale at which brain hemodynamics estimation makes sense. The voxel level is clearly not appropriate as estimating a full hemodynamic response function (HRF) from a single voxel time course may suffer from a poor signal-to-noise-ratio and lead to potentially misleading results (non-physiological HRF shapes). More robust estimation can be obtained by considering groups of voxels (i.e. parcels) with some functional homogeneity properties. Current JDE approaches are therefore based on an initial parcellation but with no guarantee of its optimality or goodness. In this work, we propose a joint parcellation-detection-estimation (JPDE) procedure that incorporates an additional parcel estimation step solving this way both the parcellation choice and robust HRF estimation issues. As in [3], inference is carried out in a Bayesian setting using variational approximation techniques for computational efficiency.

Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM definition at the subject level and makes also the classical strategy of spatial Gaussian filtering deprecated.

In this paper, a novel non-stationary model of functional Magnetic Resonance Imaging (fMRI) time series is proposed. It allows us to account for some putative habituation effect arising in event-related fMRI paradigms that involves the so-called repetition-suppression phenomenon and induces decreasing magnitude responses over successive trials. Akin to , this model is defined over functionnally homogeneous regions-of-interest (ROIs) and embedded in a joint detection-estimation approach of brain activity. Importantly, its non-stationarity character is embodied in the trial-varying nature of the BOLD response magnitude. Habituation and activation maps are then estimated within the Bayesian framework in a fully unsupervised MCMC procedure. On artificial fMRI datasets, we show that habituation effects can be accurately recovered in activating voxels.

Symmetric ising models define the simplest discrete Markov random fields that can be used for segmentation purpose. Unsupervised segmentation requires an automatic setting of the temperature parameter of ising fields. To this end, partition function (PF) estimation becomes a key issue. In this paper, we present a bilinear extrapolation technique for a fast PF estimation of 3D ising field. The proposed method is a two-step procedure that applies to the context where multiple 3D ising fields are involved over different objects (eg, brain regions) of different size and topology. First, a small set of reference PFs is accurately estimated using path sampling. Second, the large remaining set of PFs is computed using a temperature-dependent bilinear extrapolation technique. It is shown that our approach is accurate and computationally efficient to account for topological fluctuations of ising fields on regular and irregular graphs. A convincing application to joint detection-estimation of brain activity in functional MRI is also presented.

Within-subject analysis in event-related functional magnetic resonance imaging (fMRI) first relies on (i) a detection step to localize which parts of the brain are activated by a given stimulus type, and then on (ii) an estimation step to recover the temporal dynamics of the brain response. Recently, we have proposed a Bayesian detection-estimation approach that jointly addresses (i)-(ii). This approach provides both a spatial activity map and an estimate of brain dynamics. Here, we consider an extension that accounts for spatial correlation using a spatial mixture model (SMM) based on a binary Markov random field. It allows us to avoid any spatial smoothing of the data prior to the statistical analysis. Our simulation results support that SMM gives a better control of false positive (specificity) and false negative (sensitivity) rates than independent mixtures.