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Now showing 1 - 10 of 33
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    Extracting Microfacet-based BRDF Parameters from Arbitrary Materials with Power Iterations
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Dupuy, Jonathan; Heitz, Eric; Iehl, Jean-Claude; Poulin, Pierre; Ostromoukhov, Victor; Jaakko Lehtinen and Derek Nowrouzezahrai
    We introduce a novel fitting procedure that takes as input an arbitrary material, possibly anisotropic, and automatically converts it to a microfacet BRDF. Our algorithm is based on the property that the distribution of microfacets may be retrieved by solving an eigenvector problem that is built solely from backscattering samples. We show that the eigenvector associated to the largest eigenvalue is always the only solution to this problem, and compute it using the power iteration method. This approach is straightforward to implement, much faster to compute, and considerably more robust than solutions based on nonlinear optimizations. In addition, we provide simple conversion procedures of our fits into both Beckmann and GGX roughness parameters, and discuss the advantages of microfacet slope space to make our fits editable. We apply our method to measured materials from two large databases that include anisotropic materials, and demonstrate the benefits of spatially varying roughness on texture mapped geometric models.
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    Self Tuning Texture Optimization
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Kaspar, Alexandre; Neubert, Boris; Lischinski, Dani; Pauly, Mark; Kopf, Johannes; Olga Sorkine-Hornung and Michael Wimmer
    The goal of example-based texture synthesis methods is to generate arbitrarily large textures from limited exemplars in order to fit the exact dimensions and resolution required for a specific modeling task. The challenge is to faithfully capture all of the visual characteristics of the exemplar texture, without introducing obvious repetitions or unnatural looking visual elements. While existing non-parametric synthesis methods have made remarkable progress towards this goal, most such methods have been demonstrated only on relatively low-resolution exemplars. Real-world high resolution textures often contain texture details at multiple scales, which these methods have difficulty reproducing faithfully. In this work, we present a new general-purpose and fully automatic selftuning non-parametric texture synthesis method that extends Texture Optimization by introducing several key improvements that result in superior synthesis ability. Our method is able to self-tune its various parameters and weights and focuses on addressing three challenging aspects of texture synthesis: (i) irregular large scale structures are faithfully reproduced through the use of automatically generated and weighted guidance channels; (ii) repetition and smoothing of texture patches is avoided by new spatial uniformity constraints; (iii) a smart initialization strategy is used in order to improve the synthesis of regular and near-regular textures, without affecting textures that do not exhibit regularities. We demonstrate the versatility and robustness of our completely automatic approach on a variety of challenging high-resolution texture exemplars.
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    Visualizing Time-Specific Hurricane Predictions, with Uncertainty, from Storm Path Ensembles
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Liu, Le; Mirzargar, Mahsa; Kirby, Robert M.; Whitaker, Ross; House, Donald H.; H. Carr, K.-L. Ma, and G. Santucci
    The U.S. National Hurricane Center (NHC) issues advisories every six hours during the life of a hurricane. These advisories describe the current state of the storm, and its predicted path, size, and wind speed over the next five days. However, from these data alone, the question ''What is the likelihood that the storm will hit Houston with hurricane strength winds between 12:00 and 14:00 on Saturday?'' cannot be directly answered. To address this issue, the NHC has recently begun making an ensemble of potential storm paths available as part of each storm advisory. Since each path is parameterized by time, predicted values such as wind speed associated with the path can be inferred for a specific time period by analyzing the statistics of the ensemble. This paper proposes an approach for generating smooth scalar fields from such a predicted storm path ensemble, allowing the user to examine the predicted state of the storm at any chosen time. As a demonstration task, we show how our approach can be used to support a visualization tool, allowing the user to display predicted storm position - including its uncertainty - at any time in the forecast. In our approach, we estimate the likelihood of hurricane risk for a fixed time at any geospatial location by interpolating simplicial depth values in the path ensemble. Adaptivelysized radial basis functions are used to carry out the interpolation. Finally, geometric fitting is used to produce a simple graphical visualization of this likelihood. We also employ a non-linear filter, in time, to assure frame-toframe coherency in the visualization as the prediction time is advanced. We explain the underlying algorithm and definitions, and give a number of examples of how our algorithm performs for several different storm predictions, and for two different sources of predicted path ensembles.
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    Visual Analysis of Spatio-Temporal Data: Applications in Weather Forecasting
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Diehl, Alexandra; Pelorosso, Leandro; Delrieux, Claudio; Saulo, Celeste; Ruiz, Juan; Gröller, M. Eduard; Bruckner, Stefan; H. Carr, K.-L. Ma, and G. Santucci
    Weather conditions affect multiple aspects of human life such as economy, safety, security, and social activities. For this reason, weather forecast plays a major role in society. Currently weather forecasts are based on Numerical Weather Prediction (NWP) models that generate a representation of the atmospheric flow. Interactive visualization of geo-spatial data has been widely used in order to facilitate the analysis of NWP models. This paper presents a visualization system for the analysis of spatio-temporal patterns in short-term weather forecasts. For this purpose, we provide an interactive visualization interface that guides users from simple visual overviews to more advanced visualization techniques. Our solution presents multiple views that include a timeline with geo-referenced maps, an integrated webmap view, a forecast operation tool, a curve-pattern selector, spatial filters, and a linked meteogram. Two key contributions of this work are the timeline with geo-referenced maps and the curve-pattern selector. The latter provides novel functionality that allows users to specify and search for meaningful patterns in the data. The visual interface of our solution allows users to detect both possible weather trends and errors in the weather forecast model.We illustrate the usage of our solution with a series of case studies that were designed and validated in collaboration with domain experts.
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    Compressive Volume Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Liu, Xiaoyang; Alim, Usman R.; H. Carr, K.-L. Ma, and G. Santucci
    Compressive rendering refers to the process of reconstructing a full image from a small subset of the rendered pixels, thereby expediting the rendering task. In this paper, we empirically investigate three image order techniques for compressive rendering that are suitable for direct volume rendering. The first technique is based on the theory of compressed sensing and leverages the sparsity of the image gradient in the Fourier domain. The latter techniques exploit smoothness properties of the rendered image; the second technique recovers the missing pixels via a total variation minimization procedure while the third technique incorporates a smoothness prior in a variational reconstruction framework employing interpolating cubic B-splines. We compare and contrast the three techniques in terms of quality, efficiency and sensitivity to the distribution of pixels. Our results show that smoothness-based techniques significantly outperform techniques that are based on compressed sensing and are also robust in the presence of highly incomplete information. We achieve high quality recovery with as little as 20% of the pixels distributed uniformly in screen space.
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    Rule-Enhanced Transfer Function Generation for Medical Volume Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Cai, Li-Le; Nguyen, Binh P.; Chui, Chee-Kong; Ong, Sim-Heng; H. Carr, K.-L. Ma, and G. Santucci
    In volume visualization, transfer functions are used to classify the volumetric data and assign optical properties to the voxels. In general, transfer functions are generated in a transfer function space, which is the feature space constructed by data values and properties derived from the data. If volumetric objects have the same or overlapping data values, it would be difficult to separate them in the transfer function space. In this paper, we present a rule-enhanced transfer function design method that allows important structures of the volume to be more effectively separated and highlighted. We define a set of rules based on the local frequency distribution of volume attributes. A rule-selection method based on a genetic algorithm is proposed to learn the set of rules that can distinguish the user-specified target tissue from other tissues. In the rendering stage, voxels satisfying these rules are rendered with higher opacities in order to highlight the target tissue. The proposed method was tested on various volumetric datasets to enhance the visualization of important structures that are difficult to be visualized by traditional transfer function design methods. The results demonstrate the effectiveness of the proposed method.
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    Unifying Color and Texture Transfer for Predictive Appearance Manipulation
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Okura, Fumio; Vanhoey, Kenneth; Bousseau, Adrien; Efros, Alexei A.; Drettakis, George; Jaakko Lehtinen and Derek Nowrouzezahrai
    Recent color transfer methods use local information to learn the transformation from a source to an exemplar image, and then transfer this appearance change to a target image. These solutions achieve very successful results for general mood changes, e.g., changing the appearance of an image from ''sunny'' to ''overcast''. However, such methods have a hard time creating new image content, such as leaves on a bare tree. Texture transfer, on the other hand, can synthesize such content but tends to destroy image structure. We propose the first algorithm that unifies color and texture transfer, outperforming both by leveraging their respective strengths. A key novelty in our approach resides in teasing apart appearance changes that can be modeled simply as changes in color versus those that require new image content to be generated. Our method starts with an analysis phase which evaluates the success of color transfer by comparing the exemplar with the source. This analysis then drives a selective, iterative texture transfer algorithm that simultaneously predicts the success of color transfer on the target and synthesizes new content where needed. We demonstrate our unified algorithm by transferring large temporal changes between photographs, such as change of season - e.g., leaves on bare trees or piles of snow on a street - and flooding.
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    Evaluating the Quality of Face Alignment without Ground Truth
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Sheng, Kekai; Dong, Weiming; Kong, Yan; Mei, Xing; Li, Jilin; Wang, Chengjie; Huang, Feiyue; Hu, Bao-Gang; Stam, Jos and Mitra, Niloy J. and Xu, Kun
    The study of face alignment has been an area of intense research in computer vision, with its achievements widely used in computer graphics applications. The performance of various face alignment methods is often imagedependent or somewhat random because of their own strategy. This study aims to develop a method that can select an input image with good face alignment results from many results produced by a single method or multiple ones. The task is challenging because different face alignment results need to be evaluated without any ground truth. This study addresses this problem by designing a feasible feature extraction scheme to measure the quality of face alignment results. The feature is then used in various machine learning algorithms to rank different face alignment results. Our experiments show that our method is promising for ranking face alignment results and is able to pick good face alignment results, which can enhance the overall performance of a face alignment method with a random strategy. We demonstrate the usefulness of our ranking-enhanced face alignment algorithm in two practical applications: face cartoon stylization and digital face makeup.
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    Visualization of Particle-based Data with Transparency and Ambient Occlusion
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Staib, Joachim; Grottel, Sebastian; Gumhold, Stefan; H. Carr, K.-L. Ma, and G. Santucci
    Particle-based simulation techniques, like the discrete element method or molecular dynamics, are widely used in many research fields. In real-time explorative visualization it is common to render the resulting data using opaque spherical glyphs with local lighting only. Due to massive overlaps, however, inner structures of the data are often occluded rendering visual analysis impossible. Furthermore, local lighting is not sufficient as several important features like complex shapes, holes, rifts or filaments cannot be perceived well. To address both problems we present a new technique that jointly supports transparency and ambient occlusion in a consistent illumination model. Our approach is based on the emission-absorption model of volume rendering. We provide analytic solutions to the volume rendering integral for several density distributions within a spherical glyph. Compared to constant transparency our approach preserves the three-dimensional impression of the glyphs much better. We approximate ambient illumination with a fast hierarchical voxel cone-tracing approach, which builds on a new real-time voxelization of the particle data. Our implementation achieves interactive frame rates for millions of static or dynamic particles without any preprocessing. We illustrate the merits of our method on real-world data sets gaining several new insights.
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    Mosaic Drawings and Cartograms
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Cano, Rafael G.; Buchin, Kevin; Castermans, Thom; Pieterse, Astrid; Sonke, Willem; Speckmann, Bettina; H. Carr, K.-L. Ma, and G. Santucci
    Cartograms visualize quantitative data about a set of regions such as countries or states. There are several different types of cartograms and - for some - algorithms to automatically construct them exist. We focus on mosaic cartograms: cartograms that use multiples of simple tiles - usually squares or hexagons - to represent regions. Mosaic cartograms communicate well data that consist of, or can be cast into, small integer units (for example, electorial college votes). In addition, they allow users to accurately compare regions and can often maintain a (schematized) version of the input regions' shapes. We propose the first fully automated method to construct mosaic cartograms. To do so, we first introduce mosaic drawings of triangulated planar graphs. We then show how to modify mosaic drawings into mosaic cartograms with low cartographic error while maintaining correct adjacencies between regions. We validate our approach experimentally and compare to other cartogram methods.