Geometric-based Line Segment Tracking for HDR Stereo Sequences

In this work, we propose a purely geometrical approach for the robust matching of line segments for challenging stereo streams with severe illumination changes or High Dynamic Range (HDR) environments. To that purpose, we exploit the univocal nature of the matching problem, i.e. every observation must be corresponded with a single feature or not corresponded at all. We state the problem as a sparse, convex, `1-minimization of the matching vector regularized by the geometric constraints. This formulation allows for the robust tracking of line segments along sequences where traditional appearance-based matching techniques tend to fail due to dynamic changes in illumination conditions. Moreover, the proposed matching algorithm also results in a considerable speed-up of previous state of the art techniques making it suitable for real-time applications such as Visual Odometry (VO). This, of course, comes at expense of a slightly lower number of matches in comparison with appearance based methods, and also limits its application to continuous video sequences, as it is rather constrained to small pose increments between consecutive frames.We validate the claimed advantages by first evaluating the matching performance in challenging video sequences, and then testing the method in a benchmarked point and line based VO algorithm.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.This work has been supported by the Spanish Government (project DPI2017-84827-R and grant BES-2015-071606) and by the Andalucian Government (project TEP2012-530)

Indoor Calibration using Segment Chains

International audienceIn this paper, we present a new method for line segments matching for indoor reconstruction. Instead of matching individual seg- ments via a descriptor like most methods do, we match segment chains that have a distinctive topology using a dynamic programing formulation. Our method relies solely on the geometric layout of the segment chain and not on photometric or color profiles. Our tests showed that the presented method is robust and manages to produce calibration information even under a drastic change of viewpoint

Behavior Extraction from Examples Using Federate MCMC-Based Particle Filtering

AbstractData-driven methods of simulating a crowd of virtual humans that exhibit behaviors imitating real human crowds play an important role in crowd simulation. In this paper, we propose a Bayesian framework for the extraction of real human's behaviors which exhibit interactions in their daily life using multiple fixed cameras. The described Markov chain Monte Carlo particle filter can effectively deals with interacting targets which are influenced by the proximity and behaviors of other targets. In this paper, we use a Markov random field motion prior combing with a federate filter algorithm which treats the observations discriminatorily to substantially improve the tracking of a fixed number of interacting targets. Simultaneously, we replace the traditional importance sampling step with MCMC sampling step to get over the vast computational requirements for large numbers of targets. i.e., we focus on the data fusion and the behavior recognition process. Finally, experimental results demonstrate that the proposed Bayesian framework deals efficiently and effectively with extractions of interacting behavior

Obtenção de informação 3D a partir de movimento de câmara : calibração, detecção e simplificação de entidades, seguimento temporal, triangulação

A obtenção de informação tridimensional (3D) de objectos apresenta importância extrema em muitas aplicações de Visão Computacional. Como exemplos, podem ser citados:inspecção industrial, guiamento de veículos, reconstrução, seguimento e identificação de objectos. Neste artigo, é apresentada uma metodologia para obter informação 3D a partir do movimento de uma câmara, constituída pelas fases: calibração, detecção e simplificação de entidades, seguimento temporal das mesmas e obtenção de coordenadas 3D

A Maximum Likelihood Approach to Extract Polylines from 2-D Laser Range Scans

Man-made environments such as households, offices, or factory floors are typically composed of linear structures. Accordingly, polylines are a natural way to accurately represent their geometry. In this paper, we propose a novel probabilistic method to extract polylines from raw 2-D laser range scans. The key idea of our approach is to determine a set of polylines that maximizes the likelihood of a given scan. In extensive experiments carried out on publicly available real-world datasets and on simulated laser scans, we demonstrate that our method substantially outperforms existing state-of-the-art approaches in terms of accuracy, while showing comparable computational requirements. Our implementation is available under https://github.com/acschaefer/ple.Comment: 9 page

Estimation des positions d'objets 3D à partir d'une séquence d'images monoculaire

Cet article présente une méthode d'estimation des positions d'objets 3D à partir de primitives 2D extraites d'une séquence d'images monoculaire. L'estimation de la structure est ici rendue possible par l'utilisation de la modélisation de Plücker, dont l'avantage est de présenter une formulation invariante du mouvement en 2D et 3D. Cette méthode est bien adaptée à la prise en compte de tout objet que l'on peut décrire par une approximation polygonale

Détermination automatique des paramètres d'un modèle déformable de Fourier

Nous présentons dans cet article une étude sur un modèle déformable paramétrique de Fourier. Tout d'abord nous établissons, à partir du principe de moindre action de Hamilton les équations d'évolution des paramètres du modèle. Un lissage multi-échelle de l'image ainsi qu'un choix approprié des facteurs d'amortissement et d'inertie assurent la convergence du processus. Pour le suivi de primitives, la nature hiérarchique de la base de Fourier fournit un cadre original pour caractériser les composantes principales du mouvement. En dernier lieu, des résultats sont présentés sur des images synthétiques et sur des images réelles