Search

Similarity maps show dimensionality-reduced activation vectors of a high number of data points and thereby can help to understand which features a neural network has learned from the data. However, similarity maps have severely limited expressiveness for large datasets with hundreds of thousands of data instances and thousands of labels, such as ImageNet or word2vec. In this work, we present "concept splatters" as a scalable method to interactively explore similarities between data instances as learned by the machine through the lens of human-understandable semantics. Our approach enables interactive exploration of large latent spaces on multiple levels of abstraction. We present a web-based implementation that supports interactive exploration of tens of thousands of word vectors of word2vec and CNN feature vectors of ImageNet. In a qualitative study, users could effectively discover spurious learning strategies of the network, ambiguous labels, and could characterize reasons for potential confusion. [Display omitted] • Multi-scale visualization for interactive exploration of large latent spaces. • Visual encoding principle based on prototype theory. • Online interface for visual exploration of ImageNet-trained CNNs and word2vec. • Multiple use cases visualize previously reported properties of neural networks. [ABSTRACT FROM AUTHOR]

We introduce a novel method for identification of objects of interest in volume data. Our approach conveys the information contained in two essentially different concepts, the object''s boundaries and the narrow solid structures, in an easy and uniform way. The second-order derivative operators in directions reaching minimal response are employed for this task. To show the superior performance of our method, we provide a comparison with its main competitor—surface extraction from areas of maximal gradient magnitude. We show that our approach provides the possibility to represent volume data by a subset of a nominal size. Practical applications of our method include fast volume display due to object-space oriented techniques, progressive visualization over the network and the related generation of preview data sets for web-based repositories. For these applications, the size of the representative subset can be estimated automatically with respect to the bottleneck of the visualization system or network bandwidth. [Copyright &y& Elsevier]

High‐resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell substructures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. In‐depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, and supported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparative visualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges them in linked and juxtaposed views. To adapt to new domain‐specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. We present an in‐depth case study for mitochondria analysis in neuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists. [ABSTRACT FROM AUTHOR]

We propose an interactive web‐based application to inform the general public about personal flood risks. Flooding is the natural hazard affecting most people worldwide. Protection against flooding is not limited to mitigation measures, but also includes communicating its risks to affected individuals to raise awareness and preparedness for its adverse effects. Until now, this is mostly done with static and indiscriminate 2D maps of the water depth. These flood hazard maps can be difficult to interpret and the user has to derive a personal flood risk based on prior knowledge. In addition to the hazard, the flood risk has to consider the exposure of the own house and premises to high water depths and flow velocities as well as the vulnerability of particular parts. Our application is centered around an interactive personalized visualization to raise awareness of these risk factors for an object of interest. We carefully extract and show only the relevant information from large precomputed flood simulation and geospatial data to keep the visualization simple and comprehensible. To achieve this goal, we extend various existing approaches and combine them with new real‐time visualization and interaction techniques in 3D. A new view‐dependent focus+context design guides user attention and supports an intuitive interpretation of the visualization to perform predefined exploration tasks. HORA 3D enables users to individually inform themselves about their flood risks. We evaluated the user experience through a broad online survey with 87 participants of different levels of expertise, who rated the helpfulness of the application with 4.7 out of 5 on average. [ABSTRACT FROM AUTHOR]

A list of colleagues and international experts in the field of computer graphics who contributed to the writing of the journal is presented, including Pere Brunett, Dieter Fellner and Philip Dutre.

An introduction to the journal is presented in which the editors announce the appointment of new associate editors of the journal from April 2010 to March 2013, including Paolo Cignoni and Hans Hagen, the large number of article submissions received by the journal in 2009 and the end of the second term of Roberto Scopigno as chief editor of the journal.

In visualization systems it is often the case that the changes of the input parameters are not proportional to the visual change of the generated output. In this paper, we propose a model for enabling data-sensitive navigation for user-interface elements. This model is applied to normalize the user input according to the visual change, and also to visually communicate this normalization. In this way, the exploration of heterogeneous data using common interaction elements can be performed in an efficient way. We apply our model to the field of medical visualization and present guided navigation tools for traversing vascular structures and for camera rotation around 3D volumes. The presented examples demonstrate that the model scales to user-interface elements where multiple parameters are set simultaneously. [ABSTRACT FROM AUTHOR]

We investigate uncertainty guidance mechanisms to support proton therapy (PT) planning visualization. Uncertainties in the PT workflow pose significant challenges for navigating treatment plan data and selecting the most optimal plan among alternatives. Although guidance techniques have not yet been applied to PT planning scenarios, they have successfully supported sense- and decision-making processes in other contexts. We hypothesize that augmenting PT uncertainty visualization with guidance may influence the intended users' perceived confidence and provide new insights. To this end, we follow an iterative co-design process with domain experts to develop a visualization dashboard enhanced with distinct level-of-detail uncertainty guidance mechanisms. Our approach classifies uncertainty guidance into two dimensions: degree of intrusiveness and detail-orientation. Our dashboard supports the comparison of multiple treatment plans (i.e., nominal plans with their translational variations) while accounting for multiple uncertainty factors. We subsequently evaluate the designed and developed strategies by assessing perceived confidence and effectiveness during a sense- and decision-making process. Our findings indicate that uncertainty guidance in PT planning visualization does not necessarily impact the perceived confidence of the users in the process. Nonetheless, it provides new insights and raises uncertainty awareness during treatment plan selection. This observation was particularly evident for users with longer experience in PT planning. [Display omitted] • A dashboard for tumor PT plan comparison to tackle multiple sources of uncertainty. • Multi-level-of-detail guidance mechanisms to facilitate navigation of uncertainties. • An evaluation approach to validate uncertainty guidance in PT visualization. [ABSTRACT FROM AUTHOR]

The article presents information on the March 2009 issue of the periodical "Computer Graphics forum."

Maximum Intensity Projection (MIP) is a volume rendering technique which is used to visualize high-intensity structures within volumetric data. At each pixel the highest data value, which is encountered along a corresponding viewing ray is depicted. MIP is, for example, commonly used to extract vascular structures from medical data sets (angiography). Due to lack of depth information in MIP images, animation or interactive variation of viewing parameters is frequently used for investigation. Up to now no MIP algorithms exist which are of both interactive speed and high quality. In this paper we present a high-quality MIP algorithm (trilinear interpolation within cells), which is up to 50 times faster than brute-force MIP and at least 20 times faster than comparable optimized techniques. This speed-up is accomplished by using an alternative storage scheme for volume cells (sorted by value) and by removing cells which do not contribute to any MIP projection (regardless of the viewing direction) in a preprocessing step. Also, a fast maximum estimation within cells is used to further speed up the algorithm. [ABSTRACT FROM AUTHOR]

Understanding and analyzing univariate distributions of data in terms of their shapes as well as their specific characteristics, regarding gaps, spikes, or outliers, is crucial in many scientific disciplines. In this paper, we propose a design space composed of the visual channels position and color for representing accumulated distributions. The designs are a mixture of color-coded stripes with density lines. The width and coloring of the stripes is based on the applied binning technique. In a crowd-sourced experiment we explore a subspace, called the AccuStripes (i.e., "accumulated stripes") design space, consisting of nine representations. These AccuStripes designs integrate three composition strategies (color only, overlay, filled curve) with three binning techniques, one uniform (UB) and two adaptive methods, namely Bayesian Blocks (BB) and Jenks' Natural Breaks (NB). We evaluate the accuracy, efficiency, and confidence ratings of the nine AccuStripes designs for structural estimation and comparison tasks. Across all study tasks, the overlay composition was found to be most accurate and preferred by observers. Furthermore, the results demonstrate that while no binning method performed best in both identification and comparison, detection of structures using adaptive binning was the most accurate one. For validation we compared the best AccuStripes' design, i.e., the overlay composition, to line charts. Our results show that the AccuStripes' design outperformed the line charts in accuracy for all study tasks. [Display omitted] • Novel design space to represent and compare univariate data distributions. • A subspace is introduced combining color-coded stripes with density lines. • The stripes' width and coloring are defined by adaptive and uniform binning methods. • Evaluation and validation of the subspace through two crowdsource studies. [ABSTRACT FROM AUTHOR]

The spinel group minerals provide useful information regarding the geological environment in which the host rocks were formed, constituting excellent petrogenetic indicators, and guides in the search for mineral deposits of economic interest. In this article, we present the Spinel Web, a web application to visualize the chemical composition of spinel group minerals. Spinel Web integrates most of the diagrams commonly used for analyzing the chemical characteristics of the spinel group minerals. It incorporates parallel coordinates and a 3D representation of the spinel prisms. It also provides coordinated views and appropriate interactions for users to interact with their datasets. Spinel Web also supports semi-automatic categorization of the geological environment of formation through a standard Web browser. [ABSTRACT FROM AUTHOR]

• Visualization research relies on software development • Reward discrepancy exists between novel research and necessary software development • Visualization software is hard to design, assess, compare, use and maintain • Considerations on prototypes and extension/development of frameworks • We give advice about, identify provisions for and discuss software in our community. Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. We summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics. [ABSTRACT FROM AUTHOR]

Background: Studying the patterns of protein-protein interactions (PPIs) is fundamental for understanding the structure and function of protein complexes. The exploration of the vast space of possible mutual configurations of interacting proteins and their contact zones is very time consuming and requires the proteomic expert knowledge. Results: In this paper, we propose a novel tool containing a set of visual abstraction techniques for the guided exploration of PPI configuration space. It helps proteomic experts to select the most relevant configurations and explore their contact zones at different levels of detail. The system integrates a set of methods that follow and support the workflow of proteomics experts. The first visual abstraction method, the Matrix view, is based on customized interactive heat maps and provides the users with an overview of all possible residue-residue contacts in all PPI configurations and their interactive filtering. In this step, the user can traverse all input PPI configurations and obtain an overview of their interacting amino acids. Then, the models containing a particular pair of interacting amino acids can be selectively picked and traversed. Detailed information on the individual amino acids in the contact zones and their properties is presented in the Contact-Zone list-view. The list-view provides a comparative tool to rank the best models based on the similarity of their contacts to the template-structure contacts. All these techniques are interactively linked with other proposed methods, the Exploded view and the Open-Book view, which represent individual configurations in three-dimensional space. These representations solve the high overlap problem associated with many configurations. Using these views, the structural alignment of the best models can also be visually confirmed. Conclusions: We developed a system for the exploration of large sets of protein-protein complexes in a fast and intuitive way. The usefulness of our system has been tested and verified on several docking structures covering the three major types of PPIs, including coiled-coil, pocket-string, and surface-surface interactions. Our case studies prove that our tool helps to analyse and filter protein-protein complexes in a fraction of the time compared to using previously available techniques. [ABSTRACT FROM AUTHOR]

The visual analysis of surface cracks plays an essential role in tunnel maintenance when assessing the condition of a tunnel. To identify patterns of cracks, which endanger the structural integrity of its concrete surface, analysts need an integrated solution for visual analysis of geometric and multivariate data to decide if issuing a repair project is necessary. The primary contribution of this work is a design study, supporting tunnel crack analysis by tightly integrating geometric and attribute views to allow users a holistic visual analysis of geometric representations and multivariate attributes. Our secondary contribution is Visual Analytics and Rendering, a methodological approach which addresses challenges and recurring design questions in integrated systems. We evaluated the tunnel crack analysis solution in informal feedback sessions with experts from tunnel maintenance and surveying. We substantiated the derived methodology by providing guidelines and linking it to examples from the literature. [ABSTRACT FROM AUTHOR]

In situ analysis is becoming increasingly important in the evaluation of existing as well as novel materials and components. In this domain, specialists require answers on questions such as: How does a process change internal and external structures of a component? or How do the internal features evolve? In this work, we present a novel integrated visual analysis tool to evaluate series of X-ray Computed Tomography (XCT) data. We therefore process volume datasets of a series of XCT scans, which non-destructively cover the evolution of a process by in situ scans. After the extraction of individual features, a feature tracking algorithm is applied to detect changes of features throughout the series as events . We distinguish between creation, continuation, split, merge and dissipation events. As an explicit tracking is not always possible, we introduce the computation of a Tracking Uncertainty . We visualize the data together with the determined events in multiple linked-views, each emphasizing individual aspects of the 4D-XCT dataset series: A Volume Player and a 3D Data View show the spatial feature information, whereas the global overview of the feature evolution is visualized in the Event Explorer . The Event Explorer allows for interactive exploration and selection of the events of interest. The selection is further used as basis to calculate a Fuzzy Tracking Graph visualizing the global evolution of the features over the whole series. We finally demonstrate the results and advantages of the proposed tool using various real world applications, such as a wood shrinkage analysis and an AlSiC alloy under thermal load. [ABSTRACT FROM AUTHOR]

In the literature on optimal regular volume sampling, the Body-Centered Cubic (BCC) lattice has been proven to be optimal for sampling spherically band-limited signals above the Nyquist limit. On the other hand, if the sampling frequency is below the Nyquist limit, the Face-Centered Cubic (FCC) lattice was demonstrated to be optimal in reducing the prealiasing effect. In this paper, we confirm that the FCC lattice is indeed optimal in this sense in a certain interval of the sampling frequency. By theoretically estimating the prealiasing error in a realistic range of the sampling frequency, we show that in other frequency intervals, the BCC lattice and even the traditional Cartesian Cubic (CC) lattice are expected to minimize the prealiasing. The BCC lattice is superior over the FCC lattice if the sampling frequency is not significantly below the Nyquist limit. Interestingly, if the original signal is drastically undersampled, the CC lattice is expected to provide the lowest prealiasing error. Additionally, we give a comprehensible clarification that the sampling efficiency of the FCC lattice is lower than that of the BCC lattice. Although this is a well-known fact, the exact percentage has been erroneously reported in the literature. Furthermore, for the sake of an unbiased comparison, we propose to rotate the Marschner-Lobb test signal such that an undue advantage is not given to either lattice. [ABSTRACT FROM AUTHOR]

This paper addresses the increasing demand in industry for methods to analyze and visualize multimodal data involving a spectral modality. Two data modalities are used: high-resolution X-ray computed tomography (XCT) for structural characterization and low-resolution X-ray fluorescence (XRF) spectral data for elemental decomposition. We present InSpectr, an integrated tool for the interactive exploration and visual analysis of multimodal, multiscalar data. The tool has been designed around a set of tasks identified by domain experts in the fields of XCT and XRF. It supports registered single scalar and spectral datasets optionally coupled with element maps and reference spectra. InSpectr is instantiating various linked views for the integration of spatial and non-spatial information to provide insight into an industrial component's structural and material composition: views with volume renderings of composite and individual 3D element maps visualize global material composition; transfer functions defined directly on the spectral data and overlaid pie-chart glyphs show elemental composition in 2D slice-views; a representative aggregated spectrum and spectra density histograms are introduced to provide a global overview in the spectral view. Spectral magic lenses, spectrum probing and elemental composition probing of points using a pie-chart view and a periodic table view aid the local material composition analysis. Two datasets are investigated to outline the usefulness of the presented techniques: a 3D virtually created phantom with a brass metal alloy and a real-world 2D water phantom with insertions of gold, barium, and gadolinium. Additionally a detailed user evaluation of the results is provided. [ABSTRACT FROM AUTHOR]

Abstract: We present an interactive graphical approach for the explicit specification of semantics for volume visualization. This explicit and graphical specification of semantics for volumetric features allows us to visually assign meaning to both input and output parameters of the visualization mapping. This is in contrast to the implicit way of specifying semantics using transfer functions. In particular, we demonstrate how to realize a dynamic specification of semantics which allows to flexibly explore a wide range of mappings. Our approach is based on three concepts. First, we use semantic shader augmentation to automatically add rule-based rendering functionality to static visualization mappings in a shader program, while preserving the visual abstraction that the initial shader encodes. With this technique we extend recent developments that define a mapping between data attributes and visual attributes with rules, which are evaluated using fuzzy logic. Second, we let users define the semantics by analogy through brushing on renderings of the data attributes of interest. Third, the rules are specified graphically in an interface that provides visual clues for potential modifications. Together, the presented methods offer a high degree of freedom in the specification and exploration of rule-based mappings and avoid the limitations of a linguistic rule formulation. [Copyright &y& Elsevier]

Displaying a large number of lines within a limited amount of screen space is a task that is common to many different classes of visualization techniques such as time-series visualizations, parallel coordinates, link-node diagrams, and phase-space diagrams. This paper addresses the challenging problems of cluttering and overdraw inherent to such visualizations. We generate a 2×2 tensor field during line rasterization that encodes the distribution of line orientations through each image pixel. Anisotropic diffusion of a noise texture is then used to generate a dense, coherent visualization of line orientation. In order to represent features of different scales, we employ a multi-resolution representation of the tensor field. The resulting technique can easily be applied to a wide variety of line-based visualizations. We demonstrate this for parallel coordinates, a time-series visualization, and a phase-space diagram. Furthermore, we demonstrate how to integrate a focus+context approach by incorporating a second tensor field. Our approach achieves interactive rendering performance for large data sets containing millions of data items, due to its image-based nature and ease of implementation on GPUs. Simulation results from computational fluid dynamics are used to evaluate the performance and usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

Most volume rendering systems based on CPU volume raycasting still suffer from inefficient CPU utilization and high memory usage. To target these issues we present a new technique for efficient data addressing. Furthermore, we introduce a new processing scheme for volume raycasting which exploits thread-level parallelism—a technology now supported by commodity computer architectures. [Copyright &y& Elsevier]

In this paper a new gradient estimation method is presented which is based on linear regression. Previous contextual shading techniques try to fit an approximate function to a set of surface points in the neighborhood of a given voxel. Therefore a system of linear equations has to be solved using the computationally expensive Gaussian elimination. In contrast, our method approximates the density function itself in a local neighborhood with a 3D regression hyperplane. This approach also leads to a system of linear equations but we will show that it can be solved with an efficient convolution. Our method provides at each voxel location the normal vector and the translation of the regression hyperplane which are considered as a gradient and a filtered density value respectively. Therefore this technique can be used for surface smoothing and gradient estimation at the same time. [ABSTRACT FROM AUTHOR]

In this paper we present a fast visualization technique for volumetric data, which is based on a recent non-photorealistic rendering technique. Our new approach enables alternative insights into 3D data sets (compared to traditional approaches such as direct volume rendering or iso-surface rendering). Object contours, which usually are characterized by locally high gradient values, are visualized regardless of their density values. Cumbersome tuning of transfer functions, as usually needed for setting up DVR views is avoided. Instead, a small number of parameters is available to adjust the non-photorealistic display. Based on the magnitude of local gradient information as well as on the angle between viewing direction and gradient vector, data values are mapped to visual properties (color, opacity), which then are combined to form the rendered image (MIP is proposed as the default compositing stragtegy here). Due to the fast implementation of this alternative rendering approach, it is possible to interactively investigate the 3D data, and quickly learn about internal structures. Several further extensions of our new approach, such as level lines are also presented in this paper. [ABSTRACT FROM AUTHOR]