From Visualization to Association Rules : an automatic approach

International audienceThe main goal of Data Mining is the research of relevant information from a huge volume of data. It is generally achieved either by automatic algorithms or by the visual exploration of data. Thanks to algorithms, an exhaustive set of patterns matching specific measures can be found. But the volume of extracted information can be greater than the volume of initial data. Visual Data Mining allows the specialist to focus on a specific area of data that may describe interesting patterns. However, it is often limited by the difficulty to deal with a great number of multi dimensional data. In this paper, we propose to mix an automatic and a manual method, by driving the automatic extraction using a data scatter plot visualization. This visualization affects the number of rules found and their construction. We illustrate our method on two databases. The first describes one month French air traffic and the second stems from 2012 KDD Cup database

Reconstruction of Three-Dimensional Blood Vessel Model Using Fractal Interpolation

Fractal method is used in the image processing and studying the irregular and the complex shapes in the image. It is also used in the reconstruction and smoothing of one-, two-, and three-dimensional data. In this chapter, we present an interpolating fractal algorithm to reconstruct 3D blood vessels. Firstly, the proposed method determines the blood vessel centerline from the 2D retina image, and then it uses the Douglas-Peucker algorithm to detect the control points. Secondly, we use the 3D fractal interpolation and iterated function systems for the visualization and reconstruction of these blood vessels. The results showed that the obtained reduction rate is between 71 and 94% depending on the tolerance value. The 3D blood vessels model can be reconstructed efficiently by using the 3D fractal interpolation method

The 3D Acid Test: Perceptual Attributes vs Renderable Elements

The Romantics artificially embellished light and colour to convey emotion in their artworks. Light and colour were used to ignite a sense of enchantment and to stir an emotional response from the viewer. 3D software operates within this established visual tradition: current digital artistic representation involves a similarly embellished reality. This is a testament to what we continually want to see and how we would like to be visually entertained and informed, and physically based 3D renderer Arnold provides the tools for this continuation. Inherent in the world’s most-used 3D rendering programme Arnold are light and surface attributes which have been programmed to be adjustable to achieve myriad visual results. These attributes, however, have a history rooted in computer graphics’ plight for realism by abiding by the laws of optics and physics in their creation. However, these tools were designed with an arbitrarily chosen set of limits: arbitrary in the sense that these limits define a range of possibility to be used conveniently by the artist rather than by necessity or intrinsic nature. Johann Goethe (b. 1749), a Romantic poet, was critical of how light and colour were used by his artistic peers. He was dissatisfied by the embellishment of light and colour in paintings, and endeavoured to know exactly what was happening when he looked at things. Goethe conducted a series of experiments on light and colour, which resulted in his book Theory of Colours (1810, trans. Charles Eastlake, 1840). In my study, using Theory of Colours as a guideline, I have recreated fifty of Goethe’s experiments in 3D. I explore the fundamentals of Arnold as it was created, revealing the benchmark of current achievable 3D realism. Ten of these experiments are discussed in this paper. These experiments, in my judgment, are more applicable to the scope of phenomena replicable with a renderer, and scale the vast number of Goethe’s experiments in Theory of Colours to a reasonable set of testable conditions. The human perception of reality is the baseline against which rendering qualities must be judged, and Goethe’s experiments are replicable. As an instructor of 3D rendering, I aim to instill in my students the knowledge gained from this study, with the intention to empower the students with their own rendering so that they may make informed, predictable decisions

GraspDB14 – Documentation on a database of grasp motions and its creation

Motion capture, often abbreviated mocap, generally aims at recording any kind of motion -- be it from a person or an object -- and to transform it to a computer-readable format. Especially the data recorded from (professional and non-professional) human actors are typically used for analysis in e.g. medicine, sport sciences, or biomechanics for evaluation of human motion across various factors. Motion capture is also widely used in the entertainment industry: In video games and films realistic motion sequences and animations are generated through data-driven motion synthesis based on recorded motion (capture) data. Although the amount of publicly available full-body-motion capture data is growing, the research community still lacks a comparable corpus of specialty motion data such as, e.g. prehensile movements for everyday actions. On the one hand, such data can be used to enrich (hand-over animation) full-body motion capture data - usually captured without hand motion data due to the drastic dimensional difference in articulation detail. On the other hand, it provides means to classify and analyse prehensile movements with or without respect to the concrete object manipulated and to transfer the acquired knowledge to other fields of research (e.g. from 'pure' motion analysis to robotics or biomechanics). Therefore, the objective of this motion capture database is to provide well-documented, free motion capture data for research purposes. The presented database GraspDB14 in sum contains over 2000 prehensile movements of ten different non-professional actors interacting with 15 different objects. Each grasp was realised five times by each actor. The motions are systematically named containing an (anonymous) identifier for each actor as well as one for the object grasped or interacted with. The data were recorded as joint angles (and raw 8-bit sensor data) which can be transformed into positional 3D data (3D trajectories of each joint). In this document, we provide a detailed description on the GraspDB14-database as well as on its creation (for reproducibility). Chapter 2 gives a brief overview of motion capture techniques, freely available motion capture databases for both, full body motions and hand motions, and a short section on how such data is made useful and re-used. Chapter 3 describes the database recording process and details the recording setup and the recorded scenarios. It includes a list of objects and performed types of interaction. Chapter 4 covers used file formats, contents, and naming patterns. We provide various tools for parsing, conversion, and visualisation of the recorded motion sequences and document their usage in chapter 5

Generation of realistic skydome images

Generation of realistic skydome images We aim to generate realistic images of the sky with clouds using generative adversarial networks (GANs). We explore two GAN architectures, ProGAN and StyleGAN, and find that StyleGAN produces significantly better results. We also propose a novel architecture SuperGAN which aims to generate images at very high resolutions, which cannot be efficiently handled using state-of-art architectures. 1Generování realistických snímků obloh Naším cílem je generovat realistické obrázky oblohy s oblačností pomocí generativních kompetitivních sítí (GAN). Zkoumáme dvě architektury GANů, ProGAN a StyleGAN, a zjišťujeme, že StyleGAN dosahuje významně lepších výsledků. Pro generování obrázků ve velmi vysokém rozlišení, které nemůže být efektivně zpracováno soudobými architekturami GANů, navrhujeme novou architekturu SuperGAN. 1Department of Software and Computer Science EducationKatedra softwaru a výuky informatikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Représentation, modélisation et génération procédurale de terrains

Slides disponiblesSoutenance oral (présentation + questions) disponible sur demandeThis PhD (entitled "Representation, modelisation and procedural generation of terrains") is related to movie and videogames digital content creation, especially natural scenes.Our work is dedicated to handle and to generate landscapes efficently. We propose a new model based on a construction tree inside which the user can handle parts of the terrain intuitively. We also present techniques to efficently visualize such model. Finally, we present a new algorithm for generating large-scale terrains exhibiting hierarchical structures based on their hydrographic networks: elevation is generated in a broad compliance to water-tansport principles without having to resort on costly hydraulic simulations.Cette thèse (qui a pour intitulé "Représentation, modélisation et génération procédurale de terrains") a pour cadre la génération de contenus numériques destinés aux films et aux jeux-vidéos, en particulier les scènes naturelles.Nos travaux visent à représenter et à générer des terrains. Nous proposons, en particulier, un nouveau modèle de représentation qui s'appuie sur un arbre de construction et qui va permettre à l'utilisateur de manipuler des morceaux de terrain de façon intuitive. Nous présentons également des techniques pour visualiser ce modèle avec un maximum d'efficacité. Enfin nous développons un nouvel algorithme de génération de terrains qui construit de très grands reliefs possédant des structures hiérarchiques découlant d'un réseau hydrographique : le relief généré est conforme aux grands principes d'écoulement des eaux sans avoir besoin d'utiliser de coûteuses simulations d'érosion hydrique