Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images

We present an information-theoretic approach to the registration of images with directional information, and especially for diffusion-Weighted Images (DWI), with explicit optimization over the directional scale. We call it Locally Orderless Registration with Directions (LORD). We focus on normalized mutual information as a robust information-theoretic similarity measure for DWI. The framework is an extension of the LOR-DWI density-based hierarchical scale-space model that varies and optimizes the integration, spatial, directional, and intensity scales. As affine transformations are insufficient for inter-subject registration, we extend the model to non-rigid deformations. We illustrate that the proposed model deforms orientation distribution functions (ODFs) correctly and is capable of handling the classic complex challenges in DWI-registrations, such as the registration of fiber-crossings along with kissing, fanning, and interleaving fibers. Our experimental results clearly illustrate a novel promising regularizing effect, that comes from the nonlinear orientation-based cost function. We show the properties of the different image scales and, we show that including orientational information in our model makes the model better at retrieving deformations in contrast to standard scalar-based registration.Comment: 16 pages, 19 figure

A L1-TV Algorithm for Robust Perspective Photometric Stereo with Spatially-Varying Lightings

International audienceWe tackle the problem of perspective 3D-reconstruction of Lambertian surfaces through photometric stereo, in the presence of outliers to Lambert’s law, depth discontinuities, and unknown spatially-varying lightings. To this purpose, we introduce a robust L1-TV variational formulation of the recovery problem where the shape itself is the main unknown, which naturally enforces integrability and permits to avoid integrating the normal field

De l'intérêt de la texture pour la segmentation du visage

National audienceDans cet article, nous évaluons l'intérêt d'utiliser la texture pour segmenter une image de visage en zones Peau, Fond et Cheveux. Tout d'abord, nous présentons des techniques existantes de détection de peau, nous justifions le choix a priori de la texture, ensuite nous construisons des indices caractéristiques locaux à partir de données apprises sur l'image. Nous définissons un modèle de classification s'appuyant conjointement sur les données couleur et texture. Nous montrons l'apport de cette approche en présentant quelques résultats prometteurs obtenus sur des images naturelles complexes

Kernel Bundle EPDiff: Evolution Equations for Multi-Scale Diffeomorphic Image Registration

International audienceIn the LDDMM framework, optimal warps for image registration are found as end-points of critical paths for an energy functional, and the EPDiff equations describe the evolution along such paths. The Large Deformation Diffeomorphic Kernel Bundle Mapping (LDDKBM) extension of LDDMM allows scale space information to be automatically incorporated in registrations and promises to improve the standard framework in several aspects. We present the mathematical foundations of LDDKBM and derive the KB-EPDiff evolution equations, which provide optimal warps in this new framework. To illustrate the resulting diffeomorphism paths, we give examples showing the decoupled evolution across scales and how the method automatically incorporates deformation at appropriate scales

A L1-TV Algorithm for Robust Perspective Photometric Stereo with Spatially-Varying Lightings

We tackle the problem of perspective 3D-reconstruction of Lambertian surfaces through photometric stereo, in the presence of outliers to Lambert’s law, depth discontinuities, and unknown spatially-varying lightings. To this purpose, we introduce a robust L1-TV variational formulation of the recovery problem where the shape itself is the main unknown, which naturally enforces integrability and permits to avoid integrating the normal field

Multi-Valued Motion Fields Estimation for Transparent Sequences with a Variational Approach

Most optical flow algorithms provide flow fields as single valued functions of the image sequence domains. Only a very few of them attempt to recover multiple motion vectors at given location, which is necessary when some transparent layers are moving independently. In this report we introduce a novel framework for modeling multivalued motion fields, and propose an energy minimization formulation with smoothing terms and terms implementing velocity model competition. We illustrate the capabilities of this approach on synthetic and real sequences

Fusion de données RVB-D par stéréophotométrie colorée

Nous montrons comment utiliser la stéréophotométrie colorée pour améliorer le relief fourni par un capteur RVB-D. Le capteur est équipé de trois LEDs colorées, de telle sorte que l’image RVB permet de retrouver les détails les plus fins du relief, grâce à la stéréophotométrie. Cette estimation fine du relief est fusionnée avec la carte de profondeur fournie par le capteur, grâce à une nouvelle approche variationnelle de la stéréophotométrie adaptée aux sources ponctuelles anisotropes de type LED. Cette approche, qui est à la fois différentielle et variationnelle, permet d’estimer la profondeur directement et de façon robuste, sans estimation préalable des normales et de l’albédo. Elle offre donc un cadre naturel pour la prise en compte d’un a priori sur la profondeur, tel que le relief grossier fourni par le capteur RVB-D