GPU-boosted online image matching

International audienceMatching feature points between images is a key point in many Computer Vision tasks. As the number of images increases, this rapidly becomes a bottleneck. We here present how to use the power of GPUs to obtain image matching in typically 20 ms for one thousand points. This speedup makes applications like interactive image matching possible. Such a portable system, dedicated to 3D large scale reconstruction, is reported

Non-rigid Shape Matching Using Geometry and Photometry

International audienceIn this paper, we tackle the problem of finding correspondences between three-dimensional reconstructions of a deformable surface at different time steps. We suppose that (i) the mechanical underlying model imposes time-constant geodesic distances between points on the surface; and that (ii) images of the real surface are available. This is for instance the case in spatio-temporal shape from videos (e.g. multi-view stereo, visual hulls, etc.) when the surface is supposed approximatively unstretchable. These assumptions allow to exploit both geometry and photometry. In particular we propose an energy based formulation of the problem, extending the work of Bronstein et of. [1]. On the one hand, we show that photometry (i) improves accuracy in case of locally elastic deformations or noisy surfaces and (ii) allows to still find the right solution when [1] fails because of ambiguities (e.g. symmetries). On the other hand, using geometry makes it possible to match shapes that have undergone large motion, which is not possible with usual photometric methods. Numerical experiments prove the efficiency of our method on synthetic and real data

Sparse learning approach to the problem of robust estimation of camera locations

International audienceIn this paper, we propose a new approach--inspired by the recent advances in the theory of sparse learning-- to the problem of estimating camera locations when the internal parameters and the orientations of the cameras are known. Our estimator is defined as a Bayesian maximum a posteriori with multivariate Laplace prior on the vector describing the outliers. This leads to an estimator in which the fidelity to the data is measured by the L∞-norm while the regularization is done by the L1 -norm. Building on the papers [11, 15, 16, 14, 21, 22, 24, 18, 23] for L∞ -norm minimization in multiview geometry and, on the other hand, on the papers [8, 4, 7, 2, 1, 3] for sparse recovery in statistical framework, we propose a two-step procedure which, at the first step, identifies and removes the outliers and, at the second step, estimates the unknown parameters by minimizing the L∞ cost function. Both steps are fairly fast: the outlierremoval is done by solving one linear program (LP), while the final estimation is performed by a sequence of LPs. An important difference compared to many existing algorithms is that for our estimator it is not necessary to specify neither the number nor the proportion of the outliers

Extraction of Tubular Structures over an Orientation Domain

International audienceThis paper presents a new method to extract tubular structures from bi-dimensional images. The core of the proposed algorithm is the computation of geodesic curves over a four-dimensional space that includes local orientation and scale. These shortest paths follow closely the centerline of tubular structures, provide an estimation of the radius and can deal robustly with crossings over the image plane. Numerical experiments on a database of synthetic and natural images show the superiority of the proposed approach with respect to several method based on shortest paths extractions

Hierarchical shape-based surface reconstruction for dense multi-view stereo

International audienceThe recent widespread availability of urban imagery has lead to a growing demand for automatic modeling from multiple images. However, modern image-based modeling research has focused either on highly detailed reconstructions of mostly small objects or on human-assisted simplified modeling. This paper presents a novel algorithm which automatically outputs a simplified, segmented model of a scene from a set of calibrated input images, capturing its essential geometric features. Our approach combines three successive steps. First, a dense point cloud is created from sparse depth maps computed from the input images. Then, shapes are robustly extracted from this set of points. Finally, a compact model of the scene is built from a spatial subdivision induced by these structures: this model is a global minimum of an energy accounting for the visibility of the final surface. The effectiveness of our method is demonstrated through several results on both synthetic and real data sets, illustrating the various benefits of our algorithm, its robustness and its relevance for architectural scenes

Extraction of Vessels Networks over an Orientation Domain

This paper presents a new method to extract a network of vessels centerlines from a medical image. The network is composed of local geodesics over a four-dimensional space that includes local orientation and scale. These shortest paths follow closely the center of vessels and can deal robustly with crossings over the image plane. The vessel network is grown by an iterative algorithm that distributes seed points according to a geodesic saliency field. Numerical experiments on a database of synthetic and medical images show the superiority of our approach with respect to several methods based on shortest paths extractions. % With a minimum of user interaction, it allows to compute a complex network of vessels over noisy medical images

Photo-consistent surface reconstruction from noisy point clouds

International audienceExisting algorithms for surface reconstruction from point sets are defeated by moderate amounts of noise and outliers, which makes them unapplicable to point clouds originating from multi-view image data. In this paper, we present a novel method which incorporates the input images in the surface reconstruction process for a better accuracy and robustness. Our approach is based on the medial axis transform of the scene, which our algorithm estimates through a global photo-consistency optimization by simulated annealing. A faithful polyhedral representation of the scene is then obtained by inversion of the medial axis transform

3D model fitting for facial expression analysis under uncontrolled imaging conditions

International audienceThis paper addresses the recovering of 3D pose and animation of the human face in a monocular single image under uncontrolled imaging conditions. Our goal is to fit a 3D animated model in a face image with possibly large variations of head pose and facial expressions. Our data were acquired from filmed epileptic seizures of patients undergoing investigation in the videotelemetry 1unit, La Timone hospital, Marseille, France

Variational Principles, Surface Evolution, PDE's, Level Set Methods and the Stereo Problem

We present a novel geometric approach for solving the stereo problem for an arbitrary number of images (greater than or equal to 2). It is based upon the definition of a variational principle that must be satisfied by the surfaces of the objects in the scene and their images. The Euler-Lagrange equations which are deduced from the variational principle provide a set of PDE's which are used to deform an initial set of surfaces which then move towards the objects to be detected. The level set implementation of these PDE's potentially provides an efficient and robust way of achieving the surface evolution and to deal automatically with changes in the surface topology during the deformation, i.e. to deal with multiple objects. Results of a two dimensional implementation of our theory are presented on synthetic and real images

Sift-based sequence registration and flow-based cortical vessel segmentation applied to high resolution optical imaging data

International audienceSeveral functional and biomedical imaging techniques rely on determining hemodynamic variables and their changes in large vascular networks. To do so at micro-vascular resolution requires taking into account the - usually small but often non-rigid - mechanical deformations of the imaged vasculature induced by the cardiac pulsation and/or the subjects'body movements. Here, we present two new algorithmic approaches, allowing (i) to efficiently and accurately co-register large sets of such images in a non-rigid manner using Scale-Invariant Feature Transform (SIFT) keypoints, and (ii) to extract blood vessels and their diameters based on blood-flow information using a fast marching algorithm. These methods were applied to optical imaging data of intrinsic signals from awake monkey visual cortex at high spatiotemporal resolution (30μm, 5ms). The movement of red blood cells in the sequences could be enhanced by a Beer-Lambert-based image preprocessing. Our SIFT-based registration could be directly compared to a rigid registration, whereas the vessel extraction algorithm was tested by verifying flow conservation in vascular branching points. Finally, both methods together proved to improve a lot the estimation of the blood velocity in the vessels