The toulouse vanishing points dataset

International audienceIn this paper we present the Toulouse Vanishing Points Dataset, a public photographs database of Manhattan scenes taken with an iPad Air 1. The purpose of this dataset is the evaluation of vanishing points estimation algorithms. Its originality is the addition of Inertial Measurement Unit (IMU) data synchronized with the camera under the form of rotation matrices. Moreover, contrary to existing works which provide vanishing points of reference in the form of single points, we computed uncertainty regions. The Toulouse Vanishing Points Dataset is publicly available at http://ubee.enseeiht.fr/tvp

TVPD : un jeu de données pour évaluer les algorithmes d'estimation de points de fuite

International audienceNous présentons à travers cet article un nouveau jeu de données comprenant un corpus de photos de scènes dites de Manhattan prises avec un iPad Air 1. Ce jeu de données permet d'évaluer des algorithmes d'estimation de points de fuite. À la différence des jeux de données existants, des données inertielles issues des différents capteurs de l'iPad, synchronisées avec la caméra sont également présentes. Ces données inertielles peuvent être utilisées pour améliorer les performances d'un algorithme d'estimation de points de fuite. Enfin, nous présentons un nouvel algorithme d'estimation de régions d'incertitude sur les points de fuite estimés à partir des segments vérités-terrain

Feature-Based Estimation of Radial Basis Mappings for Non-Rigid Registration

We study the challenging problem of registering images of a non-rigid surface by estimating a Radial Basis Mapping from feature matches. We cast the problem as a Maximum Likelihood Estimation coupled with nested model selection. We propose an algorithm based on dynamically inserting centres and refining the transformation parameters under the control of a selection model criterion. We validate the algorithm using extensive simulations and by building on recent feature extraction and matching techniques, we report convincing results on real data

A propos d'interactions qui permettent d'analyser une vidéo

National audienceWith a massive amount of new images and videos being produced every day, understanding visual content becomes a major issue in today's research. Can users be part of this process which is usually adressed by computer vision mechanisms ? We try in this paper to answer this question. We present here a method to analyze users' behaviour when viewing interactive videos (zoomable video) to detect regions of interest (ROI). We do not analyze spatio-temporal properties of the video but only traces of users' interactions, to detect the Region of Interest. We aim at investigating in future work the different ways of combining those two methods.Face à la production massive actuelle d'images et de vidéos numériques, la compréhension automatique de contenus visuels est un enjeu majeur. Quel peut-être le rôle des utilisateurs dans ce processus d'analyse qui s'appuie, aussi, sur des mécanismes de vision artificielle ? Telle est la question centrale soulevée dans cet article. L'approche suivie ici consiste à analyser le comportement des utilisateurs lorsqu'ils visionnent des vidéos interactives (zoomables) pour détecter des régions d'intérêt (ROI). La détection de ROI s'opère ici par analyse des usages (crowdsourcing) et non par analyse des propriétés spatio-temporelles du contenu de la vidéo. Notre objectif dans des travaux futurs est de montrer la complémentarité de ces deux méthodes et d'en étudier les modalités

Optimizing Hypervideo Navigation Using a Markov Decision Process Approach

Interaction with hypermedia documents is a required feature for new sophisticated yet flexible multimedia applications. This paper presents an innovative adaptive technique to stream hypervideo that takes into account user behaviour. The objective is to optimize hypervideo prefetching in order to reduce the latency caused by the network. This technique is based on a model provided by a Markov Decision Process approach. The problem is solved using two methods: classical stochastic dynamic programming algorithms and reinforcement learning. Experimental results under stochastic network conditions are very promising

Special issue on context-aware and mobile multimedia databases and services

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Adapting Content Delivery to Limited Resources and Inferred User Interest

This paper discusses adaptation policies for information systems that are subject to dynamic and stochastic contexts such as mobile access to multimedia web sites. In our approach, adaptation agents apply sequential decisional policies under uncertainty. We focus on the modeling of such decisional processes depending on whether the context is fully or partially observable. Our case study is a movie browsing service in a mobile environment that we model by using Markov decision processes (MDPs) and partially observable MDP (POMDP). We derive adaptation policies for this service, that take into account the limited resources such as the network bandwidth. We further refine these policies according to the partially observable users' interest level estimated from implicit feedback. Our theoretical models are validated through numerous simulations

Résolution de la stéréophotométrie par apprentissage

Session "Posters"National audienceNous montrons dans cet article comment résoudre la stéréophotométrie par apprentissage. L'idée de base consiste à insérer une sphère dans la scène dont on souhaite retrouver le relief, et à recouvrir la sphère et la scène d'une même peinture. Les images de la sphère servant de données d'apprentissage, il n'est pas nécessaire de connaître la réflectance de la scène pour mettre en oeuvre la stéréophotométrie. De plus, les ombres propres sont considérées comme des données utiles. Nous montrons que l'analyse en composantes principales (ACP) permet, d'une part, d'obtenir les mêmes résultats que Hertzmann et al. tout en réduisant le temps de calcul, et d'autre part, de détecter les ombres portées de la scène, pour peu que la sphère en soit dépourvue. Nous montrons aussi comment généraliser notre approche à des scènes non préalablement peintes, mais de nature photométrique uniforme. Toutes ces contributions sont validées sur images de synthèse et images réelle

The DCP Bay: Toward an Art-House Content Delivery Network for Digital Cinema

International audienceCinema theaters have arrived in the digital era. The Digital Cinema Initiatives has chosen Digital Cinema Package(DCP) as format for the distribution of feature films. No suitable economical nor technological model is proposed for DCP content delivery to art-house theaters. The existing solutions are too expensive or not adapted. Therefore, we conduct this research activity in cooperation with Utopia cinemas, a group of art-house French cinemas. Utopia’s main requirement (besides functional ones) is to provide free and open source software for DCP distribution. In this paper, we present a Content Delivery Network for DCP adapted to art-house. This network is operative since mid 2014 and based on torrent peer-to-peer technology inside a multi-point VPN

A Video Timeline with Bookmarks and Prefetch State for Faster Video Browsing

International audienceReducing seek latency by predicting what the users will access is important for user experience, particularly during video browsing, where users seek frequently to skim through a video. Much existing research strived to predict user access pattern more accurately to improve the prefetching hit rate. This paper proposed a different approach whereby the prefetch hit rate is improved by biasing the users to seek to prefetched content with higher probability, through changing the video player user interface. Through a user study, we demonstrated that our player interface can lead to up to 4$\times$ more seeks to bookmarked segments and reduce seek latency by 40\%, compared to a video player interface commonly used today. The user study also showed that the user experience and the understanding of the video content when browsing is not compromised by the changes in seek behavior.