Concept# Dictionnaire

Résumé

thumb|upright=1.2|Dictionnaire en latin constitué de plusieurs volumes, œuvre d'Egidio Forcellini (1771).
Un dictionnaire est un ouvrage de référence contenant un ensemble de mots d’une langue ou d’un domaine d’activité généralement présentés par ordre alphabétique et fournissant pour chacun une définition, une explication ou une correspondance (synonyme, antonyme, cooccurrence, traduction, étymologie).
Le présent article concerne les dictionnaires unilingues qui décrivent ou normalisent une langue. Ceux-ci sont à distinguer d'autres types d'ouvrages de référence : les dictionnaires de noms propres ; les encyclopédies ou dictionnaire de choses ; les dictionnaires de traduction bilingues ; les dictionnaires des synonymes ; les dictionnaires thématiques spécialisés (dictionnaire du droit, du commerce, dictionnaire de géographie, dictionnaire humoristique, dictionnaire médical, etc.).
Étymologie
Le substantif masculin dictionnaire est un emprunt

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Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature values in different geographical locations, information diffusion in social networks, functional activities in the brain, or 3D meshes in computer graphics. The representation, analysis, and compression of such data is a challenging task and requires the development of new tools that can identify and properly exploit the data structure. In this thesis, we formulate the processing and analysis of structured data using the emerging framework of graph signal processing. Graphs are generic data representation forms, suitable for modeling the geometric structure of signals that live on topologically complicated domains. The vertices of the graph represent the discrete data domain, and the edge weights capture the pairwise relationships between the vertices. A graph signal is then defined as a function that assigns a real value to each vertex. Graph signal processing is a useful framework for handling efficiently such data as it takes into consideration both the signal and the graph structure. In this work, we develop new methods and study several important problems related to the representation and structure-aware processing of graph signals in both centralized and distributed settings. We focus in particular in the theory of sparse graph signal representation and its applications and we bring some insights towards better understanding the interplay between graphs and signals on graphs. First, we study a novel yet natural application of the graph signal processing framework for the representation of 3D point cloud sequences. We exploit graph-based transform signal representations for addressing the challenging problem of compression of data that is characterized by dynamic 3D positions and color attributes. Next, we depart from graph-based transform signal representations to design new overcomplete representations, or dictionaries, which are adapted to specific classes of graph signals. In particular, we address the problem of sparse representation of graph signals residing on weighted graphs by learning graph structured dictionaries that incorporate the intrinsic geometric structure of the irregular data domain and are adapted to the characteristics of the signals. Then, we move to the efficient processing of graph signals in distributed scenarios, such as sensor or camera networks, which brings important constraints in terms of communication and computation in realistic settings. In particular, we study the effect of quantization in the distributed processing of graph signals that are represented by graph spectral dictionaries and we show that the impact of the quantization depends on the graph geometry and on the structure of the spectral dictionaries. Finally, we focus on a widely used graph process, the problem of distributed average consensus in a sensor network where sensors exchange quantized information with their neighbors. We propose a novel quantization scheme that depends on the graph topology and exploits the increasing correlation between the values exchanged by the sensors throughout the iterations of the consensus algorithm.

Demand has emerged for next generation visual technologies that go beyond conventional 2D imaging. Such technologies should capture and communicate all perceptually relevant three-dimensional information about an environment to a distant observer, providing a satisfying, immersive experience. Camera networks offer a low cost solution to the acquisition of 3D visual information, by capturing multi-view images from different viewpoints. However, the camera's representation of the data is not ideal for common tasks such as data compression or 3D scene analysis, as it does not make the 3D scene geometry explicit. Image-based scene representations fundamentally require a multi-view image model that facilitates extraction of underlying geometrical relationships between the cameras and scene components. Developing new, efficient multi-view image models is thus one of the major challenges in image-based 3D scene representation methods. This dissertation focuses on defining and exploiting a new method for multi-view image representation, from which the 3D geometry information is easily extractable, and which is additionally highly compressible. The method is based on sparse image representation using an overcomplete dictionary of geometric features, where a single image is represented as a linear combination of few fundamental image structure features (edges for example). We construct the dictionary by applying a unitary operator to an analytic function, which introduces a composition of geometric transforms (translations, rotation and anisotropic scaling) to that function. The advantage of this approach is that the features across multiple views can be related with a single composition of transforms. We then establish a connection between image components and scene geometry by defining the transforms that satisfy the multi-view geometry constraint, and obtain a new geometric multi-view correlation model. We first address the construction of dictionaries for images acquired by omnidirectional cameras, which are particularly convenient for scene representation due to their wide field of view. Since most omnidirectional images can be uniquely mapped to spherical images, we form a dictionary by applying motions on the sphere, rotations, and anisotropic scaling to a function that lives on the sphere. We have used this dictionary and a sparse approximation algorithm, Matching Pursuit, for compression of omnidirectional images, and additionally for coding 3D objects represented as spherical signals. Both methods offer better rate-distortion performance than state of the art schemes at low bit rates. The novel multi-view representation method and the dictionary on the sphere are then exploited for the design of a distributed coding method for multi-view omnidirectional images. In a distributed scenario, cameras compress acquired images without communicating with each other. Using a reliable model of correlation between views, distributed coding can achieve higher compression ratios than independent compression of each image. However, the lack of a proper model has been an obstacle for distributed coding in camera networks for many years. We propose to use our geometric correlation model for distributed multi-view image coding with side information. The encoder employs a coset coding strategy, developed by dictionary partitioning based on atom shape similarity and multi-view geometry constraints. Our method results in significant rate savings compared to independent coding. An additional contribution of the proposed correlation model is that it gives information about the scene geometry, leading to a new camera pose estimation method using an extremely small amount of data from each camera. Finally, we develop a method for learning stereo visual dictionaries based on the new multi-view image model. Although dictionary learning for still images has received a lot of attention recently, dictionary learning for stereo images has been investigated only sparingly. Our method maximizes the likelihood that a set of natural stereo images is efficiently represented with selected stereo dictionaries, where the multi-view geometry constraint is included in the probabilistic modeling. Experimental results demonstrate that including the geometric constraints in learning leads to stereo dictionaries that give both better distributed stereo matching and approximation properties than randomly selected dictionaries. We show that learning dictionaries for optimal scene representation based on the novel correlation model improves the camera pose estimation and that it can be beneficial for distributed coding.

By incorporating computational methods into the image acquisition pipeline, computational photography has opened up new avenues in the representation and visualization of real world objects in the digital world. For example, we can sample a scene under a few specialized illuminations and a sparse set of viewpoints. We can later, computationally recover the complete light transport properties of the scene. Once we obtain the light transport characterization of cultural artifacts, we can enable users of virtual museums to interact with the artifacts in the same way as we experience these objects in the physical world. In particular, in this thesis, we develop algorithms and tools that facilitate the acquisition of relightable photographs of cultural artifacts, by acquiring their light transport matrix (LTM). A recurrent theme in this thesis is to exploit the low dimensionality of the LTM to develop efficient acquisition strategies for image based rendering. First, we propose a new acquisition and modeling framework for inverse rendering of stained glass windows. Stained glass windows are a dynamic art form that change their appearance constantly, due to the ever-changing outdoor illumination. They are therefore, an exceptional candidate for virtual relighting. However, as they are anchored and very large in size, it is often impossible to sample their entire light transport with controlled illumination. We build a material specific dictionary by studying the scattering properties of glass samples and exploiting the structure of their LTMs in a laboratory setup. We then pose the estimation of the LTM of stained glass from a small set of photographic observations, as a linear inverse problem that is constrained by sparsity in the custom dictionary. We show by experiments that our proposed solution preserves volume impurities under both controlled and uncontrolled, natural illuminations and that the retrieved LTM is heterogeneous, as in the case of real world objects. Next, equipped just with a dictionary to describe light transport in stained glass, we focus on the problem of designing a meaningful LTM, for the synthetic rendering of stained glass. Since this is an extremely ill-posed problem, we begin by exploring the physical properties of glass that can be used as constraints in light transport design. We then propose an iterative matrix completion algorithm that generates the LTM of a heterogeneous glass slab, given the dictionary and the physical constraints. We use this synthesis algorithm, in combination with an input texture to simulate stained glass windows in scenarios where inverse rendering is impossible or as an artist's preview tool. We also introduce a framework for the digital restoration of broken slabs of glass by first acquiring the LTM with inverse rendering and then using the proposed matrix completion framework to repair the fractures. Finally, we present an easy-to-use, handheld acquisition framework to sample the LTM of more general, reflective scenes. We first non-uniformly sample the scene reflectance by moving the LED attached to a smartphone along an arbitrary trajectory, while simultaneously tracking the position of the LED. The acquired reflectance is resampled to obtain a sparse set of samples on a uniform lattice. Using a compressive sensing framework, we recover an approximation to the uniformly sampled LTM, that is then used in scene relighting.

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