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Now showing 1 - 10 of 203
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    A Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Series
    (The Eurographics Association and Blackwell Publishing Ltd., 2011) Hao, M. C.; Janetzko, H.; Mittelstädt, S.; Hill, W.; Dayal, U.; Keim, D. A.; Marwah, M.; Sharma, R. K.; H. Hauser, H. Pfister, and J. J. van Wijk
    Time series prediction methods are used on a daily basis by analysts for making important decisions. Most of these methods use some variant of moving averages to reduce the number of data points before prediction. However, to reach a good prediction in certain applications (e.g., power consumption time series in data centers) it is important to preserve peaks and their patterns. In this paper, we introduce automated peak-preserving smoothing and prediction algorithms, enabling a reliable long term prediction for seasonal data, and combine them with an advanced visual interface: (1) using high resolution cell-based time series to explore seasonal patterns, (2) adding new visual interaction techniques (multi-scaling, slider, and brushing & linking) to incorporate human expert knowledge, and (3) providing both new visual accuracy color indicators for validating the predicted results and certainty bands communicating the uncertainty of the prediction. We have integrated these techniques into a wellfitted solution to support the prediction process, and applied and evaluated the approach to predict both power consumption and server utilization in data centers with 70-80% accuracy.
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    Camera Motion Graphs
    (The Eurographics Association, 2014) Sanokho, Cunka Bassirou; Desoche, Clement; Merabti, Billal; Li, Tsai-yen; Christie, Marc; Vladlen Koltun and Eftychios Sifakis
    This paper presents Camera Motion Graphs, a technique to easily and efficiently generate cinematographic sequences in real-time dynamic 3D environments. A camera motion graph consists of (i) pieces of original camera trajectories attached to one or multiple targets, (ii) generated continuous transitions between camera trajectories and (iii) transitions representing cuts between camera trajectories. Pieces of original camera trajectories are built by extracting camera motions from real movies using vision-based techniques, or relying on motion capture techniques using a virtual camera system. A transformation is proposed to recompute all the camera trajectories in a normalized representation, making camera paths easily adaptable to new 3D environments through a specific retargeting technique. The camera motion graph is then constructed by sampling all pairs of camera trajectories and evaluating the possibility and quality of continuous or cut transitions. Results illustrate the simplicity of the technique, its adaptability to different 3D environments and its efficiency.
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    Example-based Haze Removal with two-layer Gaussian Process Regressions
    (The Eurographics Association, 2014) Fan, Xin; Gao, Renjie; Wang, Yi; John Keyser and Young J. Kim and Peter Wonka
    Hazy images suffer from low visibility and contrast. Researchers have devoted great efforts to haze removal with the prior assumptions on observations in the past decade. However, these priors from observations can provide limited information for the restoration of high quality, and the assumptions are not always true for generic images in practice. On the other hand, visual data are increasing as the popularity of imaging devices. In this paper, we present a learning framework for haze removal based on two-layer Gaussian Process Regressions (GPR). By using training examples, the two-layer GPRs establish direct relationships from the input image to the depth-dependent transmission, and meanwhile learn local image priors to further improve the estimation. We also provide a method to collect training pairs for images of natural scenes. Both qualitative and quantitative comparisons on simulated and real-world hazy images demonstrate the effectiveness of the approach, especially when white or bright objects and heavy haze regions appear and existing dehazing methods may fail.
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    Nonparametric Models for Uncertainty Visualization
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Pöthkow, Kai; Hege, Hans-Christian; B. Preim, P. Rheingans, and H. Theisel
    An uncertain (scalar, vector, tensor) field is usually perceived as a discrete random field with a priori unknown probability distributions. To compute derived probabilities, e.g. for the occurrence of certain features, an appropriate probabilistic model has to be selected. The majority of previous approaches in uncertainty visualization were restricted to Gaussian fields. In this paper we extend these approaches to nonparametric models, which are much more flexible, as they can represent various types of distributions, including multimodal and skewed ones. We present three examples of nonparametric representations: (a) empirical distributions, (b) histograms and (c) kernel density estimates (KDE). While the first is a direct representation of the ensemble data, the latter two use reconstructed probability density functions of continuous random variables. For KDE we propose an approach to compute valid consistent marginal distributions and to efficiently capture correlations using a principal component transformation. Furthermore, we use automatic bandwidth selection, obtaining a model for probabilistic local feature extraction. The methods are demonstrated by computing probabilities of level crossings, critical points and vortex cores in simulated biofluid dynamics and climate data.
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    ViewFusion: Correlating Structure and Activity Views for Execution Traces
    (The Eurographics Association, 2012) Trümper, Jonas; Telea, Alexandru; Döllner, Jürgen; Hamish Carr and Silvester Czanner
    Visualization of data on structure and related temporal activity supports the analysis of correlations between the two types of data. This is typically done by linked views. This has shortcomings with respect to efficient space usage and makes mapping the effect of user input into one view into the other view difficult. We propose here a novel, space-efficient technique that 'fuses' the two information spaces - structure and activity - in one view. We base our technique on the idea that user interaction should be simple, yet easy to understand and follow. We apply our technique, implemented in a prototype tool, for the understanding of software engineering datasets, namely static structure and execution traces of the Chromium web browser.
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    GPU Visualization and Voxelization of Yarn-Level Cloth
    (The Eurographics Association, 2014) Lopez-Moreno, Jorge; Cirio, Gabriel; Miraut, David; Otaduy, Miguel Angel; Adolfo Munoz and Pere-Pau Vazquez
    Most popular methods in cloth rendering rely on volumetric data in order to model complex optical phenomena such as sub-surface scattering. Previous work represents yarns as a sequence of identical but rotated crosssections. While these approaches are able to produce very realistic illumination models, the required volumetric representation is difficult to compute and render, forfeiting any interactive feedback. In this paper, we introduce a method based on the GPU for simultaneous visualization and voxelization, suitable for both interactive and offline rendering. Our method can interactively voxelize millions of polygons into a 3D texture, generating a volume with sub-voxel accuracy which is suitable even for high-density weaving such as linen.
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    From the Patient to the Surgery - A Complete Computer Vision and Graphics Process
    (The Eurographics Association, 2012) Martinez, A.; Jimenez, J.; Isabel Navazo and Gustavo Patow
    Generating digital information ready to use on a surgery operation from a patient is a complex process. This process involves computer vision techniques, in order to extract digital information from medical images, and computer graphics techniques in order to generate proper models to interact with the digital information. In this approach a set of techniques covering both fields (computer vision and graphics) have been used to develop a complete process from the medical image to the surgery training.
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    Towards Efficient Online Compression of Incrementally Acquired Point Clouds
    (The Eurographics Association, 2014) Golla, Tim; Schwartz, Christopher; Klein, Reinhard; Jan Bender and Arjan Kuijper and Tatiana von Landesberger and Holger Theisel and Philipp Urban
    We present a framework for the online compression of incrementally acquired point cloud data. For this, we extend an existing vector quantization-based offline point cloud compression algorithm to handle the challenges that arise from the envisioned online scenario. In particular, we learn a codebook in advance from training data and replace a computationally demanding part of the algorithm with a faster alternative. We show that the compression ratios and reconstruction quality are comparable to the offline version while the speed is sufficiently improved. Furthermore, we investigate how well codebooks that are generated from different amounts of training data generalize to larger sets of point cloud data.
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    Freeform Shadow Boundary Editing
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Mattausch, Oliver; Igarashi, Takeo; Wimmer, Michael; I. Navazo, P. Poulin
    We present an algorithm for artistically modifying physically based shadows. With our tool, an artist can directly edit the shadow boundaries in the scene in an intuitive fashion similar to freeform curve editing. Our algorithm then makes these shadow edits consistent with respect to varying light directions and scene configurations, by creating a shadow mesh from the new silhouettes. The shadow mesh helps a modified shadow volume algorithm cast shadows that conform to the artistic shadow boundary edits, while providing plausible interaction with dynamic environments, including animation of both characters and light sources. Our algorithm provides significantly more fine-grained local and direct control than previous artistic light editing methods, which makes it simple to adjust the shadows in a scene to reach a particular effect, or to create interesting shadow shapes and shadow animations. All cases are handled with a single intuitive interface, be it soft shadows, or (self-)shadows on arbitrary receivers.
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    Interactive Comparative Visualization of Multimodal Brain Tumor Segmentation Data
    (The Eurographics Association, 2013) Lindemann, Florian; Laukamp, Kai; Jacobs, Andreas H.; Hinrichs, Klaus; Michael Bronstein and Jean Favre and Kai Hormann
    We present a visualization system for the analysis of multi-modal segmentation data of brain tumors. Our system is designed to allow researchers and doctors a further investigation of segmented tumor data beyond a quantitative assessment of size. This includes the efficient visual analysis of the shape and relative position of the different, often overlapping segmented data modalities, using high quality 3D renderings of the data. Furthermore, our system provides visualization methods to compare tumor segmentation volumes acquired at various points of time, which helps the user to explore changes in shape and size before and after treatment. We also employ two novel interactive diagrams which allow the user to quickly navigate and analyze overlapping tumor regions. All methods are assembled and linked in a multi-view framework.