Piecewise-Planar 3D Reconstruction with Edge and Corner Regularization

International audienceThis paper presents a method for the 3D reconstruction of a piecewise-planar surface from range images, typi-cally laser scans with millions of points. The reconstructed surface is a watertight polygonal mesh that conforms to observations at a given scale in the visible planar parts of the scene, and that is plausible in hidden parts. We formulate surface reconstruction as a discrete optimization problem based on detected and hypothesized planes. One of our major contributions, besides a treatment of data anisotropy and novel surface hypotheses, is a regu-larization of the reconstructed surface w.r.t. the length of edges and the number of corners. Compared to classical area-based regularization, it better captures surface complexity and is therefore better suited for man-made en-vironments, such as buildings. To handle the underlying higher-order potentials, that are problematic for MRF optimizers, we formulate minimization as a sparse mixed-integer linear programming problem and obtain an ap-proximate solution using a simple relaxation. Experiments show that it is fast and reaches near-optimal solutions

Improving a Constraint Programming Approach for Parameter Estimation

International audienceThe parameter estimation problem is a widespread and challenging problem in engineering sciences consisting in computing the parameters of a parametric model that fit observed data. Calibration or geolocation can be viewed as specific parameter estimation problems. In this paper we address the problem of finding all the instances of a parametric model that can explain at least q observations within a given tolerance. The computer vision community has proposed the RANSAC algorithm to deal with outliers in the observed data. This randomized algorithm is efficient but non-deterministic and therefore incomplete. Jaulin et al. proposes a complete and combinatorial algorithm that exhaustively traverses the whole space of parameter vectors to extract the valid model instances. This algorithm is based on interval constraint programming methods and on a so called q-intersection operator, a relaxed intersection operator that assumes that at least q observed data are inliers. This paper proposes several improvements to Jaulin et al.'s algorithm. Most of them are generic and some others are dedicated to the shape detection problem used to validate our approach. Compared to Jaulin et al.'s algorithm, our algorithm can guarantee a number of fitted observations in the produced model instances. Also, first experiments in plane and circle recognition highlight speedups of two orders of magnitude

An Interval Branch and Bound Algorithm for Parameter Estimation

International audienceThe parameter estimation problem is a widespread and challenging problem in engineering sciences consisting in computing the parameters of a parametric model that fit observed data. The computer vision community has proposed the RANSAC algorithm to deal with outliers in the observed data. This randomized algorithm is efficient but non-deterministic and therefore incomplete. Jaulin et al. propose a branch-and-contract algorithm that returns all the model instances fitting at least q observations. Assuming that at least q observed data are inliers, this algorithm achieves on the observations a relaxed intersection operator called q-intersection. First, this paper presents several improvements to Jaulin et al.'s algorithm. Second, an interval branch and bound algorithm is designed to produce a model that can explain the maximum number of observations within a given tolerance. Experiments are carried out on computer vision and image processing problems. They highlight a significant speedup w.r.t. Jaulin et al.'s interval method in 2D and 3D shape recognition problems. We have also investigated how the approach scales up in dimensions up to 7 for stereovision (estimation of essential and fundamental matrices)

Comparing Notes: Recording and Criticism

This chapter charts the ways in which recording has changed the nature of music criticism. It both provides an overview of the history of recording and music criticism, from the advent of Edison’s Phonograph to the present day, and examines the issues arising from this new technology and the consequent transformation of critical thought and practice

Wider Still and Wider: British Music Criticism since the Second World War

This chapter provides the first historical examination of music criticism in Britain since the Second World War. In the process, it also challenges the simplistic prevailing view of this being a period of decline from a golden age in music criticism

Suivi automatique de la main à partir de séquences vidéo monoculaires

In this thesis we propose two methods that allow to recover automatically a full description of the 3d motion of a hand given a monocular video sequence of this hand. Using the information provided by the video, our aimto is to determine the full set of kinematic parameters that are required to describe the pose of the skeleton of the hand. This set of parameters is composed of the angles associate to each joint/articulation and the global position and orientation of the wrist. This problem is extremely challenging. The hand as many degrees of freedom and auto-occlusion are ubiquitous, which makes difficult the estimation of occluded or partially ocluded hand parts.In this thesis, we introduce two novel methods of increasing complexity that improve to certain extend the state-of-the-art for monocular hand tracking problem. Both are model-based methods and are based on a hand model that is fitted to the image. This process is guided by an objective function that defines some image-based measure of the hand projection given the model parameters. The fitting process is achieved through an iterative refinement technique that is based on gradient-descent and aims a minimizing the objective function. The two methos differ mainly by the choice of the hand model and of the cost function.The first method relies on a hand model made of ellipsoids and a simple discrepancy measure based on global color distributions of the hand and the background. The second method uses a triangulated surface model with texture and shading and exploits a robust distance between the synthetic and observed image as discrepancy measure.While computing the gradient of the discrepancy measure, a particular attention is given to terms related to the changes of visibility of the surface near self occlusion boundaries that are neglected in existing formulations. Our hand tracking method is not real-time, which makes interactive applications not yet possible. Increase of computation power of computers and improvement of our method might make real-time attainable.Dans cette thèse sont présentées deux méthodes visant à obtenir automatiquement une description tridimensionnelle des mouvements d'une main étant donnée une séquence vidéo monoculaire de cette main. En utilisant l'information fournie par la vidéo, l'objectif est de déterminer l'ensemble des paramètres cinématiques nécessaires à la description de la configuration spatiale des différentes parties de la main. Cet ensemble de paramètres est composé des angles de chaque articulation ainsi que de la position et de l'orientation globale du poignet. Ce problème est un problème difficile. La main a de nombreux degrés de liberté et les auto-occultations sont omniprésentes, ce qui rend difficile l'estimation de la configuration des parties partiellement ou totalement cachées. Dans cette thèse sont proposées deux nouvelles méthodes qui améliorent par certains aspects l'état de l'art pour ce problème. Ces deux méthodes sont basées sur un modèle de la main dont la configuration spatiale est ajustée pour que sa projection dans l'image corresponde au mieux à l'image de main observée. Ce processus est guidé par une fonction de coût qui définit une mesure quantitative de la qualité de l'alignement de la projection du modèle avec l'image observée. La procédure d'ajustement du modèle est réalisée grâce à un raffinement itératif de type descente de gradient quasi-newton qui vise à minimiser cette fonction de coût.Les deux méthodes proposées diffèrent principalement par le choix du modèle et de la fonction du coût. La première méthode repose sur un modèle de la main composé d'ellipsoïdes et d'une fonction coût utilisant un modèle de la distribution statistique de la couleur la main et du fond de l'image.La seconde méthode repose sur un modèle triangulé de la surface de la main qui est texturé est ombragé. La fonction de coût mesure directement, pixel par pixel, la différence entre l'image observée et l'image synthétique obtenue par projection du modèle de la main dans l'image. Lors du calcul du gradient de la fonction de coût, une attention particulière a été portée aux termes dûs aux changements de visibilité de la surface au voisinage des auto-occultations, termes qui ont été négligés dans les méthodes préexistantes.Ces deux méthodes ne fonctionnement malheureusement pas en temps réel, ce qui rend leur utilisation pour l'instant impossible dans un contexte d'interaction homme-machine. L'amélioration de la performance des ordinateur combinée avec une amélioration de ces méthodes pourrait éventuellement permettre d'obtenir un résultat en temps réel

Hand Poste Estimation with Constrained Multi-hypotheses Gradient-Descent

In this report, we detail a novel approach to recover 3D hand pose from 2D images. To this end, we introduce a compact 3D hand model in a low dimension space where anatomy, kinematics and dynamics are implicitly inherited. The parameters of this model are recovered through a Bayesian inference approach. To this end, we propose an objective function which aims at separating the hand-skin characteristics within the 2D hand silhouette from the cluttered background. To address computational issues a polygonal approximation of the silhouette is considered and the differentiations from the 3D model to the 2D silhouette projection are carried out. Optimization of the cost function is done through a smart particle filtering approach which combines classical particle filters and local search. We further develop this concept towards reducing the number of hypotheses to be tested - while retaining its performance - through the use of a constrained variable metric gradient descent step. Very promising experimental results demonstrate the potentials of our approach