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In this paper, we propose a method for coarse camera pose computation which is robust to viewing conditions and does not require a detailed model of the scene. This method meets the growing need of easy deployment of robotics or augmented reality applications in any environments, especially those for which no accurate 3D model nor huge amount of ground truth data are available. It exploits the ability of deep learning techniques to reliably detect objects regardless of viewing conditions. Previous works have also shown that abstracting the geometry of a scene of objects by an ellipsoid cloud allows to compute the camera pose accurately enough for various application needs. Though promising, these approaches use the ellipses fitted to the detection bounding boxes as an approximation of the imaged objects. In this paper, we go one step further and propose a learning-based method which detects improved elliptic approximations of objects which are coherent with the 3D ellipsoid in terms of perspective projection. Experiments prove that the accuracy of the computed pose significantly increases thanks to our method and is more robust to the variability of the boundaries of the detection boxes. This is achieved with very little effort in terms of training data acquisition -- a few hundred calibrated images of which only three need manual object annotation. Code and models are released at https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization., Comment: Presented at 3DV 2020. Code and models released at https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization

International audience; We present the use of a new computationaly efficient 3D physics model for the simulation of cells in a virtual aquatic world. In this model, cells can freely assemble and disconnect along the simulation without any separation between the development and evaluation stages, as is the case in most evo-devo models which only consider one cell cluster. While allowing for the discovery of interesting behaviors through the addition of new degrees of freedom, this 3D center-based physics engine and its associated virtual world also come with their drawbacks when applied to evolutionnary experiments: larger search space and numerous local optima. In this paper, we have designed an experiment in which cells must learn to survive by keeping their genome alive as long as possible in a demanding world. No morphology or strategy is explicitly enforced; the only objective the cells have to optimize is the survival time of the organism they build. We show that a novelty metric, adapted to our evo-devo matter, dramatically improves the outcome of the evolutionary runs. This paper also details some of the developmental strategies the evolved multicellular organisms have found in order to survive.

The placement of Virtual Network Function - Forwarding Graphs (VNF-FGs) is one of the basic operations in the networks of the future. Being NP-hard, several heuristics and metaheuristics have been proposed. However, these approaches are inefficient due to the need to recalculate the solution at each service placement. In this paper, we adapt one of the most advanced approaches in Deep Reinforcement Learning (DRL), in order to improve exploration by generalizing the neural network calculating action values. We also propose an evolutionary algorithm to evolve these neural networks in order to discover better ones, which also avoids getting stuck in local minima. In order to avoid going through the almost innumerable number of infeasible solutions, we propose a heuristic, which combined with our DRL, makes it possible to guarantee the feasibility of the solutions and therefore to make the placement much more efficient. The simulation results we obtained confirm the quality of the solutions obtained as well as the superiority of the proposed solution over the existing one.

International audience; In this paper, we present a medical image integrity verification system to detect and approximate local malevolent image alterations (e.g. removal or addition of lesions) as well as identifying the nature of a global processing an image may have undergone (e.g. lossy compression, filtering ...). The proposed integrity analysis process is based on non significant region watermarking with signatures extracted from different pixel blocks of interest and which are compared with the recomputed ones at the verification stage. A set of three signatures is proposed. The two firsts devoted to detection and modification location are cryptographic hashes and checksums, while the last one is issued from the image moment theory. In this paper, we first show how geometric moments can be used to approximate any local modification by its nearest generalized 2D Gaussian. We then demonstrate how ratios between original and recomputed geometric moments can be used as image features in a classifier based strategy in order to determine the nature of a global image processing. Experimental results considering both local and global modifications in MRI and retina images illustrate the overall performances of our approach. With a pixel block signature of about 200 bit long, it is possible to detect, to roughly localize and to get an idea about the image tamper.

National audience; Real-time adaptive production control in the flexible manufacturing cell (FMC) is a complex issue that needs to be addressed to realize good performance and high productivity. In this paper, we have considered a support vector machine (SVM)-based simulation approach to resolve a production control problem in an FMC that operates in a dynamic environment. A SVM-based simulation approach chooses the most relevant scheduling rule out of several predefined ones on the basis of the current states of the system. This paper examines and compares the performance of the SVM-based simulation approach with the competent scheduling rules under two different operational environments which are characterized by the uncertainty of demand. We have also developed a Visual Basic-based simulation approach for scheduling of component parts in the context of FMC under different situations. The SVM methodology to control the production offers better performance than the single-rule-based production control system.

International audience; Several separate displays are used by Air Traffic Controllers, such as radar view, flight lists view or paper strips. In order to link the information between these views and keep focus on a subset of flights, controllers are required to do visual operations (eye gaze, analysis of visual properties etc.). This process can be disruptive when traffic increases and when visualizations display large amounts of objects. In this paper we propose the use of animated transitions to replace the visual paths controllers take. We discuss this technique and show results of a predictive evaluation that suggests an improvement in users' performance.

In this paper, the duality in differential form is developed between a 3D primal surface and its dual manifold formed by the surface's tangent planes, that is, each tangent plane of the primal surface is represented as a four-dimensional vector that constitutes a point on the dual manifold. The iterated dual theorem shows that each tangent plane of the dual manifold corresponds to a point on the original 3D surface, that is, the "dual" of the "dual" goes back to the "primal." This theorem can be directly used to reconstruct 3D surface from image edges by estimating the dual manifold from these edges. In this paper, we further develop the work in our original conference papers resulting in the robust differential dual operator. We argue that the operator makes good use of the information available in the image data by using both points of intensity discontinuity and their edge directions; we provide a simple physical interpretation of what the abstract algorithm is actually estimating and why it makes sense in terms of estimation accuracy; our algorithm operates on all edges in the images, including silhouette edges, self occlusion edges, and texture edges, without distinguishing their types (thus, resulting in improved accuracy and handling locally concave surface estimation if texture edges are present); the algorithm automatically handles various degeneracies; and the algorithm incorporates new methodologies for implementing the required operations such as appropriately relating edges in pairs of images, evaluating and using the algorithm's sensitivity to noise to determine the accuracy of an estimated 3D point. Experiments with both synthetic and real images demonstrate that the operator is accurate, robust to degeneracies and noise, and general for reconstructing free-form objects from occluding edges and texture edges detected in calibrated images or video sequences.

International audience; Genetic Regulatory Networks (GRNs) implementations have a high degree of variability in their details. Parameters, encoding methods, and dynamics formulas all differ in the literature, and some GRN implementations have a high degree of model complexity. In this paper, we present a comparative study of different implementations of a GRN and introduce new variants for comparison. We use a modified Genetic Algorithm (GA) to evaluate GRN performance on a number of common benchmark tasks, with a focus on real-time control problems. We propose an encoding scheme and set of dynamics equations that simplifies implementation and evaluate the evolutionary fitness of this proposed method. Lastly, we use the comparative modifications study to demonstrate overall enhancements for GRN models.

International audience; In desktop 3D modeling applications, object manipulation relies on the use of standard transformation widgets. However, these widgets are less efficient on touchscreens due to the low accuracy of touch inputs, and also to the fingertip occlusion issue. In this paper, we propose two unimanual constrained manipulation techniques on TabletPCs. Based on finger identification, our techniques map the index, middle and ring fingers of the dominant hand to the X, Y and Z axes of the coordinate system. After registering the axis-fingers mapping on the screen, users can perform different chording gestures to trigger axis-, plane-constrained or uniform manipulations. We have conducted a user study to examine the learnability and usability of the proposed gestures. The results indicate that these gestures can be memorized quickly after a short training period. Users found some gestures to be easy to use and comfortable while others require additional investigations. These findings permits to give recommendations for designing a unimanual constrained manipulation technique that can be used efficiently in a 3D modeling application on TabletPCs.; Dans les applications de modélisation 3D sur PC, la manipulation avec contraintes d'objets repose sur l’utilisation des widgets de transformation 3D. Cependant, ces widgets sont moins efficaces pour une utilisation sur des écrans tactiles en raison de la faible précision des dispositifs d’entrée et des problèmes d'occultation. Dans ce papier, deux nouvelles techniques uni-manuelles de manipulation 3D sur TabletPC sont proposées. Basées sur l’identification des doigts, nos deux techniques associent l'index, le majeur et l'annulaire de la main dominante aux axes x, y et z du système de coordonnées. Après l’enregistrement de cette association axe-doigts, les utilisateurs peuvent effectuer différents gestes d’accords pour des manipulations avec contraintes sur les axes, sur les plans et des manipulations uniformes. Nous avons mené une étude préliminaire pour examiner l'apprenabilité et l’utilisabilité des gestes proposées. Les résultats indiquent que ces derniers peuvent être mémorisés rapidement. Certains gestes ont été jugés confortables et faciles à utiliser par les utilisateurs alors que d’autres nécessitent davantage d’investigations. Ces résultats permettent de donner des recommandations quant à la conception d’une technique uni-manuelle de manipulation avec contraintes qui peut être utilisée efficacement pour la modélisation 3D sur TabletPC.

International audience; In this paper, we show how a dangerousness metric can be used to modify the input of a gene regulatory network when plugged to a virtual car. In the context of the 2015 Simulated Car Racing Championship organized during GECCO 2015, we have developed a new cartography methodology able to inform the controller of the car about the incoming complexity of the track: turns (slipperiness, angle, etc.) and bumps. We show how this dangerousness metric improves the results of our controller and outperforms other approaches on the tracks used in the competition.

TAAABLE is a Case-Based Reasoning (CBR) system that uses a recipe book as a case base to answer cooking queries. TAAABLE participates in the Computer Cooking Contest since 2008, and succeeds thanks to a smart combination of various knowledge-based system methods and techniques, such as: CBR, knowledge representation, acquisition, discovery and management, and natural language processing. In this paper, we first present the CBR engine and its features such as the retrieval process based on minimal generalization of a query and different adaptation processes. Then the knowledge containers used by the system, and how they were acquired is detailed. To conclude, we discuss some research developments resulting from this application.

National audience; In order to be used in Ambiant Intelligence, automated planning has to generate highlevel action plans involving control structures for execution controlers, which role is to play these plans with respect to events perceived in the environment. In our approach of "flexible" planning, environment non determinism is managed by plan control structures. In this paper, we explore how to express and to generate high-level plans for deterministic planning problems by defining the operational and denotational semantics of new operators for plan composition. Our Lambda GraphPlan (LGP) planner incorporates into plans iterations representing nondeterministic choices among a set of resources subject to the same abstract treatment. LGP is a planning-graph based algorithm that extracts patterns of actions whose scheduling is indifferent with respect to goal reachability and aggregates them into iterative structures. We show that LGP can be highly efficient when the solution plans incorporate iterative structures.

cote interne IRCAM: Mitsufuji14a; None / None; National audience; This paper proposes a new method to enhance the performance of non-negative tensor factorization (NTF), one of the most prevalent source separation techniques nowadays. The enhancement is mainly achieved by introducing weights on bin-wise NTF cost functions, which differentiates NTF target components from other components so that the target should be approximated more precisely than others. Assuming sources are distributed sparsely in a 2-D sound field, the target components approximating a target source are exclusively selected by a user, or from accompanying images by means of providing a spatial cue to an NTF framework. The spatial cue is given in a similar format to the well-known binaural feature, inter-channel level difference (IID). This helps incorporate the spatial cue into the system, since the similar features of this format can be easily calculated from every spectrogram bin. The weighting functions are designed taking into account the distance between the spatial cue and the calculated features. Namely, the largest values are assigned to the spectrogram bins where the features present the highest similarity to the spatial cue, and the value decreases in proportion to the distance between them. The method is evaluated in terms of separation quality, comparing the proposed algorithm to the conventional NTF technique, PARAFAC-NTF, as well as other source separation techniques. The evaluation results measured by the metric signal-to-distortion ratio (SDR), signal-to-interference ratio (SIR), and signal-to-artifact ratio (SAR) demonstrate the effectiveness of the new method, improved primarily by the weighting function and the initialization based on IID, while demonstrating a decrease in computational costs, a significant problem with NTF.

National audience; Recently government agencies have started to utilize innovative contracting methods that provide incentives for improving construction quality. These emerging contracting methods place an enormous pressure on the contractors to improve construction quality. For a general contractor, which subcontracts most tasks of a project and invites a number of bids, choosing an appropriate bid which satisfies the time, cost and quality of construction project is complex and challenging. To solve this problem involving conflicting objectives, a fuzzy clustering-based genetic algorithm (FCGA) approach is proposed in this paper. A case study of highway construction is used to demonstrate the applicability of the proposed approach. A comparative study is conducted over three test cases involving varying dimensions and complexities to test performance of the proposed FCGA against existing approaches. Results reveal that the FCGA is capable of generating better Pareto front than other existing approaches.

Novelty-based segmentation of audio signals has proven good performances for the estimation of boundaries of structural sections within music pieces. However, boundaries are detected only if structural sections satisfy the condition of sufficient acoustic inner-homogeneity. While this constraint is very restrictive and not representative of all musical contents, we propose in this paper to extend the detection of acoustic novelty to transitions between homogeneous and non-homogeneous sections and vice versa. Moreover, the length of the considered sections for the boundary detection is crucial, we also introduce a multi-scale novelty approach that allows to capture boundaries between sections of different temporal scales in a same segmentation. Evaluation of the combination of these two methods proves convincing results for temporal segmentation of music pieces. Embedding the algorithm in a music structure segmentation system, we show that performances can be consistently improved for this task.

In this paper, we introduce a new approach to music structure segmentation that is based on the joint estimation of structural segments, keys and chords in one probabilistic framework. More precisely, the boundaries of a structure segment are determined by detecting key changes and by utilizing the difference in prior probability of chord transitions according to their position in a structural segment. In contrast to many of the recent approaches to structural segmentation, this system does not work with self-similarity matrices, although it has been designed to integrate this kind of approach into the framework at a later stage. However, just the current version of the system, using only the estimated harmony, is already producing encouraging results, especially with respect to the precise localization of the boundaries.

International audience; Metamorphic robots are robots that can change their shape by reorganizing the connectivity of their modules to adapt to new environments, perform new tasks, or recover from damages. In this paper we present a decentralized method for structural evolving of a class of lattice-based simulated metamorphic robots in a static environment. These robots are considered as a set of crystalline (compressible) modules that are able to connect or disconnect one from each another or even exchange information and energy with the neighbor modules in order to form various structures/patterns dynamically. Our approach is splitted in two layers: in the first layer a genetic algorithm is used to generate a number of well suited target configurations based on current information perceived from environment, while in the second layer a PacMan-like algorithm is used to make a plan for modules movement to transform the robot from its current pattern to the target pattern emerged in first layer.

National audience; Multi-touch interaction requires a trade-off between users' desires and capabilities, and gesture recognition constraints. Current approaches to that problem lack flexibility. The number of fingers used for a gesture usually plays a key part in the recognition process, for example. In this paper, we propose the use of pseudo-rigid movements to increase the flexibility of this process. We show how these movements can be determined in real time from the contact information usually available. We explain how they allow to free the recognition process from the number of fingers used and to move towards multi movement gestures, independent or coordinated. Finally, we present an interaction technique that takes advantage of this increased flexibility.

This paper presents preliminary results of a study aimed at evaluating the effect of different ways of presenting points of interests (POI) in an augmented reality system on distance estimation. Each presentation applies specific combination of depth cues (relative size, occlusion, height, partial occlusion). Preliminary results from seven participants indicate that depth cues included in the design of the POI reduces the underestimation of the distances of the POI. The interfaces that facilitate distance estimation are those that rely both on the use of height in the visual field and relative size but not on occlusion.

cote interne IRCAM: Lanchantin11a; None / None; National audience; Hidden Markov chains (HMC) are a very powerful tool in hidden data restoration and are currently used to solve a wide range of problems. However, when these data are not stationary, estimating the parameters, which are required for unsupervised processing, poses a problem. Moreover, taking into account correlated non-Gaussian noise is difficult without model approximations. The aim of this paper is to propose a simultaneous solution to both of these problems using triplet Markov chains (TMC) and copulas. The interest of the proposed models and related processing is validated by different experiments some of which are related to semi-supervised and unsupervised image segmentation.

149-152; International audience; Current window managers are based on the use of overlapping windows, which implies the notion of a window's depth. In order to facilitate the management of windows in desktop environments, recent work has explored their grouping into layers of non-overlapping windows. In this note, we investigate the effect of three visual depth cues on the perception of the depth of such layers: luminosity, blurring and shadow. The paper reports on a controlled experiment that indicates that luminosity helps users to assess the number of layers while blurring and shadow have no effect.

This paper present a study on the diagnoses of blood cell nuclei from patients affected by the Progeria disease. Nuclei are characterized with various methods that analyze them shape and texture, and subsequently classify them by learning techniques. These methods extract "measures" from nuclei, which are used for the computation of a set of values named "indexes". These indexes are function bringing up real or discrete values that give information about geometrical, morphological and textural features of nuclei. A description model of nuclei is built based on this data, that is used to classify nuclei.

National audience; The development of micro and nanotechnologies requires the design of microrobots and Microsystems able to handle submillimetric objects. Many micromanipulation systems have been built. However, improvement of the performances requires the use of advanced control methods. In this paper, we present the main characteristics of the microworld from a control point of view and some control strategies adapted to the microsystems.

This article deals with logical and graphical representations of uncertain information in a quantitative possibilistic theory framework. We first provide a deep analysis of relationships between these two forms of representational frameworks. Then, we provide a novel algorithm for reasoning with quantitative possibilistic logic. The algorithm exploits the syntactic relations between quantitative possibilistic logic bases and penalty logic bases. We provide experimental results that compare the propagation algorithm developed for the possibilistic product-based networks and the inference algorithm developed in this paper. quantitative possibilistic theory, penalty logic, uncertainty propagation algorithm.

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]MOTIVE [Coll_IRSTEA]Series: Lecture Notes in Computer Science; National audience; Models for representing multidimensional systems usually consider that facts and dimensions are two different things. In this paper we propose a model based on UML which unifies the representations of fact and dimension member. Since a given element can play the role of a fact or a dimension member, this model permits more flexibility in the design and the implementation of multidimensional systems. Moreover this model offers the possibility to express various constraints to guarantee desirable properties for data. We then show that this model is able to handle most of the hierarchies which have been suggested to take real situations into account and to characterize certain properties of summarizability. Using this model we propose a complete development cycle of a multidimensional system. It appears that this cycle can be partially automated and that an end user can control the design and the implementation of his system himself.

In the last decade, several image and video quality metrics have been proposed, which incorporate perceptual quality measures by considering the HVS characteristics. All this metrics do the differences pixel to pixel in image. Therefore a local fidelity of the colour is defined. However, the human visual system is rather sensitive to a global quality. In this paper, we propose a new objective full reference quality metric for colour images called Global Delta E and noted: ~ G DE . This metric is based on human visual system properties in order to obtain the best correspondence with judgments. Some experimentations and assessments prove the performance of our metrics and that interrelationship with the SVH., L'étude du système visuel Humain (SVH) est primordiale pour quantifier la qualité d'une image. Plusieurs travaux ont proposé des métriques de différences de couleurs qui effectuent des différences locales pixel par pixel. Par contre, le système visuel humain est plutôt sensible à une qualité globale. Dans cet article, nous proposons une nouvelle métrique objective de différence couleur basée sur les caractéristiques du système visuel humain. La métrique proposé est appelée Delta E Globale notée : ~ DEG . Quelques expérimentations et évaluations montrent la performance de cette métrique et sa corrélation avec la vision du SVH.

International audience; This paper proposes a local, cooperative and real-time multi-agent approach to build adaptive and incremental profiles where a user is supposed to be represented by a set of textual documents. These documents are sequentially parsed, which leads to the construction of a temporary terminological network (TTN). This network is then merged with the TTN resulting from other documents, in order to create a permanent terminological network (PTN). This PTN is representative of the studied collection and can be used to index this collection thanks to a clustering approach. Preliminary results of the built system as well as perspectives are then presented.; Ce papier propose une approche multi-agent locale, coopérative et temps réel pour la création de profils adaptatifs et incrémentaux dans laquelle un usager est supposé être représenté par un ensemble de documents textuels. Ces documents sont analysés séquentiellement conduisant alors à la création d’un réseau terminologique temporaire (RTT). Ce réseau est ensuite fusionné avec les RTT issus d’autres documents, afin de générer un réseau terminologique permanent (RTP) représentatif de la collection étudiée et pouvant être ensuite indexé pour en extraire un profil. Les résultats préliminaires obtenus ainsi que les perspectives associées sont enfin présentés.

24 pages; National audience; There are many process models for information systems engineering, based on different concepts (activity, product, decision, context, strategy) and then on different process meta-models. These meta-models allow a partial vision of the process engineering. This paper presents a design pattern to model concepts of various process meta-models. This pattern is a first step towards the construction of unified, fitted and multi-views process meta-models.

In this paper we propose a control scheme based on Coloured Petri net (CPN) for a flow-rack automated storage and retrieval system. The AS/RS is modelled using Coloured Petri net. The developed model is used to capture and provide the rack state. We introduce in the control system an optimization module as decision process which performs a real-time optimization working on discrete events time scale. The objective is to find bin locations for the retrieval requests by minimizing the total number of retrieval cycles for a batch of requests and thereby increase the system throughput. By solving the optimization model, the proposed method gives according to customers request and the rack state, the best bin locations for retrieval, i.e. allowing at the same time to satisfy the customers request and carrying out the minimum of retrieval cycles.

cote interne IRCAM: Lanchantin08b; None / None; National audience; The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable noisy version. Classically, the distribution p(y|x) is simple enough to ensure the Markovianity of p(x|y), that enables one to use different Bayesian restoration techniques. HMC model has recently been extended to "pairwise Markov chain" (PMC) model, in which one directly assumes the Markovianity of the pair Z=(X,Y), and which still enables one to recover X from Y. Finally, PMC has been extended to "triplet Markov chain" (TMC) model, which is obtained by adding a third chain U and considering the Markovianity of the triplet T=(X,U,Y). When U is not too complex, X can still be recovered from Y. Then U can model different situations, like non-stationarity or semi-Markovianity of (X,Y). Otherwise, PMC and TMC have been extended to pairwise "partially" Markov chains (PPMC) and triplet "partially" Markov chains (TPMC), respectively. In a PPMC Z=(X,Y) the distribution p(x|y) is a Markov distribution, but p(y|x) may not be and, similarly, in a TPMC T=(X,U,Y) the distribution p(x,u|y) is a Markov distribution, but p(y|x,u) may not be. However, both PPMC and TPMC can enable one to recover X from Y, and TPMC include different long-memory noises. The aim of this paper is to show how a particular Gaussian TPMC can be used to segment a discrete signal hidden with long-memory noise. An original parameter estimation method, based on "Iterative Conditional Estimation" (ICE) principle, is proposed and some experiments concerned with unsupervised segmentation are provided. The particular unsupervised segmentation method used in experiments can also be seen as identification of different stationarities in fractional Brownian noise, which is widely used in different problems in telecommunications, economics, finance, or hydrology.

Content based image retrieval using spatial image content (i.e. using multiple regions and their spatial relationships) is still an open problem which has received considerable attention in literature. In this paper we introduce a new representation of image based on the most general spatial image content representation that is Attributed Relation Graphs (ARG) representation and also a new method of image indexation. Like all CBIR systems, the one proposed here has two components: a segmentation component and a matching component using a novel inexact graph matching algorithm. We tested our work in lettrines image but it's also valid in general image.

In this paper, a graph classification approach based on a multi-objective genetic algorithm is presented. The method consists in the learning of sets composed of synthetic graph prototypes which are used for a classification step. These learning graphs are generated by simultaneously maximizing the recognition rate while minimizing the confusion rate. Using such an approach the algorithm provides a range of solutions, the couples (confusion, recognition) which suit to the needs of the system. Experiments are performed on real data sets, representing 10 symbols. These tests demonstrate the interest to produce prototypes instead of finding representatives which simply belong to the data set.

cote interne IRCAM: Schwarz07a; None / None; National audience; Corpus-based concatenative methods for musical sound synthesis have attracted much attention recently. They make use of a variety of sound snippets in a database to assemble a desired sound or phrase according to a target specification given in sound descriptors or by an example sound. With ever-larger sound databases easily available, together with a pertinent description of their contents, they are increasingly used for composition, high-level instrument synthesis, interactive exploration of a sound corpus, and others. This paper gives an overview of the components needed for corpus-based concatenative synthesis, and details of some realisations. Signal processing methods are crucial for all parts of analysis, (segmentation and descriptor analysis), for synthesis, and can intervene in the selection part e.g. for spectral matching.

In most works on negotiation dialogues, agents are supposed to be ideally honest. However, there are many situations where such a behaviour cannot always be expected from the agents (e.g. advertising, political negotiation, etc.). The aim of this paper is to reconsider the role of deceitful arguments in argumentation frameworks. We propose a logical tool for representing and handling deceitful arguments in a dialogue between two formal agents having to face their respective knowledge and trying to convince each other. X-logics, a nonmonotonic extension of classical propositional logics, is used as the background formalism for representing the reasoning of the agents on arguments. Starting from a previous work dedicated to the generation of new arguments, we propose to define the notion of lie as a new kind of possible agent's answer. Finally, we describe the way an agent may trick and how the other agent may detect it

Nowadays, materialized views are increasingly being supported by a variety of commercial DBMS to speed up query response time. This technique is also very useful in dataware-housing for optimizing OLAP queries. However, most existing view selection methods are of static nature. Moreover, none of these methods have considered the problem of dematerializing the previously materialized views. This paper deals with the problem of dynamic view selection and the pending issue of removing materialized views in order to replace the less beneficial views with more beneficial views. More precisely, we have designed and implemented a view selection method, including a polynomial algorithm, to decide which views to materialize according to statistic metadata.

The digital text representation model used by the industry today is the model proposed by the Unicode standard. This model, based on the concepts of character and glyph, provides the foundations for the so-called smart font technologies such as AAT, OpenType and Graphite. Using a formal description of Unicode's text model, the authors compare the three said font formats, especially regarding paragraph breaking and how mapping information is preserved between characters and glyphs, in other words, between content and presentation. In the second part of the paper, the authors propose a novel approach to these two problems through an alternative text model based on the texteme concept and dynamic typography. MOTS-CLES : Unicode, fontes intelligentes, modele de texte, caractere, glyphe, paragraphage, texteme, typographie dynamique, OpenType, AAT, Graphite

Periodicity estimation of an audio signal, for applications such as pitch, multiple pitch or tempo estimation is often problematic due to the presence of multiple harmonics in the audio signal producing octave errors. While pitch models or rhythm models can be used, they remain often dedicated to a specific problem. In this paper, we propose a straightforward approach for periodicity estimation based on the combination of a spectral representation and a temporal representation. This method allows a better emphasis on the frequencies corresponding to the various pitches. We show the ability of this representation to adequately estimate pitch and visualize signals with multiple pitch content.

International audience; Venous thrombosis screening exams use 2D ultrasound images, from which medical experts obtain a rough idea of the thrombosis aspect and infer an approximate volume. Such estimation is essential to follow up the thrombosis evolution. This paper proposes a method to calculate venous thrombosis volume from non-parallel 2D ultrasound images, taking advantage of a priori knowledge about the thrombosis shape. An interactive ellipse fitting contour segmentation extracts the 2D thrombosis contours. Then, a Delaunay triangulation is applied to the set of 2D segmented contours positioned in 3D, and the area that each contour defines, to obtain a global thrombosis 3D surface reconstruction, with a dense triangulation inside the contours. Volume is calculated from the obtained surface and contours triangulation, using a maximum unit normal component approach. Preliminary results obtained on 3 plastic phantoms and 3 in vitro venous thromboses, as well as one in vivo case are presented and discussed. An error rate of volume estimation inferior to 4,5% for the plastic phantoms, and 3,5% for the in vitro venous thromboses was obtained.

International audience; We present in this paper a multifractal bayesian denoising technique based on an interactive EA. The multifractal denoising algorithm that serves as a basis for this technique is adapted to complex images and signals, and depends on a set of parameters. As the tuning of these parameters is a difficult task, highly dependent on psychovisual and subjective factors, we propose to use an interactive EA to drive this process. Comparative denoising results are presented with automatic and interactive EA optimisation. The proposed technique yield efficient denoising in many cases, comparable to classical denoising techniques. The versatility of the interactive implementation is however a major advantage to handle difficult images like IR or medical images.

National audience; The reliability of critical real time distributed applications must be guaranty by formal techniques of validation, as the model-checking. However these techniques often lead to combinatory explosion problems. This paper proposes efficient abstractions of the timed automata model of TTA (Time-Triggered Architecture) in a temporal validation context.; La fiabilité des applications distribuées temps réel critiques doit être garantie par des techniques formelles de validation, comme le model-checking. Cependant, ces méthodes ont souvent des problèmes d’explosion combinatoire. Cet article propose des abstractions efficaces pour la modélisation et la validation temporelle de l’'architecture TTA (Time-Triggered Architecture) avec des automates temporisés (UPPAAL).

International audience; The purpose of this paper is to present an intelligent atlas of indexed endoscopic lesions that could be used in com- puter-assisted diagnosis as reference data. The development of such a system requires a mix of medical and engineering skills for analyzing and reproducing the cognitive processes that un- derlie the medical decision-making process. The analysis of both endoscopists experience and endoscopic terminologies developed by professional associations shows that diagnostic reasoning in digestive endoscopy uses a scene-object approach. The objects correspond to the endoscopic findings and the medical context of examination and the scene to the endoscopic diagnosis. According to expert assessment, the classes of endoscopic findings and diagnoses, their primitive characteristics (or indices), and their relationships have been listed. Each class describes an endoscopic finding or diagnosis in an intensive way. The retrieval method is based on a similarity metric that estimates the membership value of the case under investigation and the prototype of the class. A simulation test with randomized objects demonstrates a good classification of endoscopic findings. The correct class is the unique response in 68% of the tested objects, the first of multiple responses in 28%. Four descriptors are shown to be of major im- portance in the classification algorithm: anatomic location, shape, color, and relief. At the present time, the application database contains approximately 150 endoscopic images and is accessible via Internet. Experiments are in progress with endoscopists for the validation of the system and for the understanding of the similarity between images. The next step will integrate the system in a learning tool for junior endoscopists.

International audience; This paper presents a general approack both syntactic and semantic, for fusing prioritized bases where priorities are encoded in the possibilistic logic framework First a survey of différent classes of possibilistic merging operators is provided, analyzing the behaviors of conjunctive and disjunctive operators, having or not a reinforrement effect. We then show that approaches which have been recently proposed for fusing, propositional bases can be easily encoded with possibilistic setting. This has two, advantages: first, the result of merging is a prioritized base, hence the merging process can be decomposed and iterated in a coherent way. Moreover it provides a syntactic counterpart to the merging of propositional bases.; Cet article présente une approche générale, syntaxique et sémantique, pour la fusion de bases propositionnelles avec priorités explicites, ces dernières étant représentées dans le cadre de la logique possibiliste. Dans un premier temps, nous présentons différentes classes d'opérateurs de fusion possibilistes conjonctifs ou disjonctes, ayant ou non des effets de renforcement, etc. Nous montrons ensuite que les approches qui ont été récemment proposées pour la fusion des bases propositionnelles sans priorités peuvent être facilement exprimées dans le cadre possibiliste. Cela a deux avantages majeurs : le résultat de la fusion est une base avec priorités explicites, et par conséquent le processus de fusion peut être itéré d'une manière cohérente. De plus, on fournit une contrepartie syntaxique, de la fusion de bases propositionnelles généralement définie de manière sémantique.

International audience; This paper describes an original approach for content-based video indexing and retrieval. We aim at providing a global interpretation of the dynamic content of video shots without any prior motion segmentation and without any use of dense optic flow fields. To this end, we exploit the spatio-temporal distribution, within a shot, of appropriate local motion-related measurements derived from the spatio-temporal derivatives of the intensity function. These distributions are then represented by causal Gibbs models. To be independent of camera movement, the motion-related measurements are computed in the image sequence generated by compensating the estimated dominant image motion in the original sequence. The statistical modeling framework considered makes the exact computation of the conditional likelihood of a video shot belonging to a given motion or more generally to an activity class feasible. This property allows us to develop a general statistical framework for video indexing and retrieval with query-by-example. We build a hierarchical structure of the processed video database according to motion content similarity. This results in a binary tree where each node is associated to an estimated causal Gibbs model. We consider a similarity measure inspired from Kullback-Leibler divergence. Then, retrieval with query-by-example is performed through this binary tree using the maximum a posteriori (MAP) criterion. We have obtained promising results on a set of various real image sequences

In this paper, the issue of document structuring is addressed. To achieve this task, we advocate that Segmented Discourse Representation Theory (SDRT) is a most expressive discourse framework. Then we sketch a discourse planning mechanism which aims at producingas many paraphrastic document structures as possible from a set of factual data encoded into a logical form.