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We present an assessment of the state and historic development of evaluation practices as reported in papers published at the IEEE Visualization conference. Our goal is to reflect on a meta-level about evaluation in our community through a systematic understanding of the characteristics and goals of presented evaluations. For this purpose we conducted a systematic review of ten years of evaluations in the published papers using and extending a coding scheme previously established by Lam et al. [2012]. The results of our review include an overview of the most common evaluation goals in the community, how they evolved over time, and how they contrast or align to those of the IEEE Information Visualization conference. In particular, we found that evaluations specific to assessing resulting images and algorithm performance are the most prevalent (with consistently 80-90% of all papers since 1997). However, especially over the last six years there is a steady increase in evaluation methods that include participants, either by evaluating their performances and subjective feedback or by evaluating their work practices and their improved analysis and reasoning capabilities using visual tools. Up to 2010, this trend in the IEEE Visualization conference was much more pronounced than in the IEEE Information Visualization conference which only showed an increasing percentage of evaluation through user performance and experience testing. Since 2011, however, also papers in IEEE Information Visualization show such an increase of evaluations of work practices and analysis as well as reasoning using visual tools. Further, we found that generally the studies reporting requirements analyses and domain-specific work practices are too informally reported which hinders cross-comparison and lowers external validity.

In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans., (© 2011 IEEE)

The Body-Centered Cubic (BCC) and Face-Centered Cubic (FCC) lattices have been analytically shown to be more efficient sampling lattices than the traditional Cartesian Cubic (CC) lattice, but there has been no estimate of their visual comparability. Two perceptual studies (each with N = 12 participants) compared the visual quality of images rendered from BCC and FCC lattices to images rendered from the CC lattice. Images were generated from two signals: the commonly used Marschner-Lobb synthetic function and a computed tomography scan of a fish tail. Observers found that BCC and FCC could produce images of comparable visual quality to CC, using 30-35 percent fewer samples. For the images used in our studies, the L(2) error metric shows high correlation with the judgement of human observers. Using the L(2) metric as a proxy, the results of the experiments appear to extend across a wide range of images and parameter choices., (© 2011 IEEE)

The problem of finding a simple, generally applicable description of worldwide measured ambient dose equivalent rates at aviation altitudes between 8 and 12 km is difficult to solve due to the large variety of functional forms and parametrisations that are possible. We present an approach that uses Bayesian statistics and Monte Carlo methods to fit mathematical models to a large set of data and to compare the different models. About 2500 data points measured in the periods 1997-1999 and 2003-2006 were used. Since the data cover wide ranges of barometric altitude, vertical cut-off rigidity and phases in the solar cycle 23, we developed functions which depend on these three variables. Whereas the dependence on the vertical cut-off rigidity is described by an exponential, the dependences on barometric altitude and solar activity may be approximated by linear functions in the ranges under consideration. Therefore, a simple Taylor expansion was used to define different models and to investigate the relevance of the different expansion coefficients. With the method presented here, it is possible to obtain probability distributions for each expansion coefficient and thus to extract reliable uncertainties even for the dose rate evaluated. The resulting function agrees well with new measurements made at fixed geographic positions and during long haul flights covering a wide range of latitudes.

In this paper we investigate the effects of function composition in the form g(f(x)) = h(x) by means of a spectral analysis of h. We decompose the spectral description of h(x) into a scalar product of the spectral description of g(x) and a term that solely depends on f(x) and that is independent of g(x). We then use the method of stationary phase to derive the essential maximum frequency of g(f(x)) bounding the main portion of the energy of its spectrum. This limit is the product of the maximum frequency of g(x) and the maximum derivative of f(x). This leads to a proper sampling of the composition h of the two functions g and f. We apply our theoretical results to a fundamental open problem in volume rendering-the proper sampling of the rendering integral after the application of a transfer function. In particular, we demonstrate how the sampling criterion can be incorporated in adaptive ray integration, visualization with multi-dimensional transfer functions, and pre-integrated volume rendering.

We review schemes for dividing cubic cells into simplices (tetrahedra) for interpolating from sampled data to IR3, present visual and geometric artifacts generated in isosurfaces and volume renderings, and discuss how these artifacts relate to the filter kernels corresponding to the subdivision schemes.

To make a spectral representation of color practicable for volume rendering, a new low-dimensional subspace method is used to act as the carrier of spectral information. With that model, spectral light material interaction can be integrated into existing volume rendering methods at almost no penalty. In addition, slow rendering methods can profit from the new technique of postillumination-generating spectral images in real-time for arbitrary light spectra under a fixed viewpoint. Thus, the capability of spectral rendering to create distinct impressions of a scene under different lighting conditions is established as a method of real-time interaction. Although we use an achromatic opacity in our rendering, we show how spectral rendering permits different data set features to be emphasized or hidden as long as they have not been entirely obscured. The use of postillumination is an order of magnitude faster than changing the transfer function and repeating the projection step. To put the user in control of the spectral visualization, we devise a new widget, a "light-dial," for interactively changing the illumination and include a usability study of this new light space exploration tool. Applied to spectral transfer functions, different lights bring out or hide specific qualities of the data. In conjunction with postillumination, this provides a new means for preparing data for visualization and forms a new degree of freedom for guided exploration of volumetric data sets.

We present a user interface, based on parallel coordinates, that facilitates exploration of volume data. By explicitly representing the visualization parameter space, the interface provides an overview of rendering options and enables users to easily explore different parameters. Rendered images are stored in an integrated history bar that facilitates backtracking to previous visualization options. Initial usability testing showed clear agreement between users and experts of various backgrounds (usability, graphic design, volume visualization, and medical physics) that the proposed user interface is a valuable data exploration tool.

Visualization can provide valuable assistance for data analysis and decision making tasks. However, how people perceive and interact with a visualization tool can strongly influence their understanding of the data as well as the system's usefulness. Human factors therefore contribute significantly to the visualization process and should play an important role in the design and evaluation of visualization tools. Several research initiatives have begun to explore human factors in visualization, particularly in perception-based design. Nonetheless, visualization work involving human factors is in its infancy, and many potentially promising areas have yet to be explored. Therefore, this paper aims to 1) review known methodology for doing human factors research, with specific emphasis on visualization, 2) review current human factors research in visualization to provide a basis for future investigation, and 3) identify promising areas for future research.

We present visual analysis methods for the evaluation of tomographic fiber reconstruction algorithms by means of analysis, visual debugging and comparison of reconstructed fibers in materials science. The methods are integrated in a tool (FIAKER) that supports the entire workflow. It enables the analysis of various fiber reconstruction algorithms, of differently parameterized fiber reconstruction algorithms and of individual steps in iterative fiber reconstruction algorithms. Insight into the performance of fiber reconstruction algorithms is obtained by a list‐based ranking interface. A 3D view offers interactive visualization techniques to gain deeper insight, e.g., into the aggregated quality of the examined fiber reconstruction algorithms and parameterizations. The tool was designed in close collaboration with researchers who work with fiber‐reinforced polymers on a daily basis and develop algorithms for tomographic reconstruction and characterization of such materials. We evaluate the tool using synthetic datasets as well as tomograms of real materials. Five case studies certify the usefulness of the tool, showing that it significantly accelerates the analysis and provides valuable insights that make it possible to improve the fiber reconstruction algorithms. The main contribution of the paper is the well‐considered combination of methods and their seamless integration into a visual tool that supports the entire workflow. Further findings result from the analysis of (dis‐)similarity measures for fibers as well as from the discussion of design decisions. It is also shown that the generality of the analytical methods allows a wider range of applications, such as the application in pore space analysis. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present Hypersliceplorer, an algorithm for generating 2D slices of multi‐dimensional shapes defined by a simplical mesh. Often, slices are generated by using a parametric form and then constraining parameters to view the slice. In our case, we developed an algorithm to slice a simplical mesh of any number of dimensions with a two‐dimensional slice. In order to get a global appreciation of the multi‐dimensional object, we show multiple slices by sampling a number of different slicing points and projecting the slices into a single view per dimension pair. These slices are shown in an interactive viewer which can switch between a global view (all slices) and a local view (single slice). We show how this method can be used to study regular polytopes, differences between spaces of polynomials, and multi‐objective optimization surfaces. [ABSTRACT FROM AUTHOR]

We present GEMSe, an interactive tool for exploring and analyzing the parameter space of multi-channel segmentation algorithms. Our targeted user group are domain experts who are not necessarily segmentation specialists. GEMSe allows the exploration of the space of possible parameter combinations for a segmentation framework and its ensemble of results. Users start with sampling the parameter space and computing the corresponding segmentations. A hierarchically clustered image tree provides an overview of variations in the resulting space of label images. Details are provided through exemplary images from the selected cluster and histograms visualizing the parameters and the derived output in the selected cluster. The correlation between parameters and derived output as well as the effect of parameter changes can be explored through interactive filtering and scatter plots. We evaluate the usefulness of GEMSe through expert reviews and case studies based on three different kinds of datasets: A synthetic dataset emulating the combination of 3D X-ray computed tomography with data from K-Edge spectroscopy, a three-channel scan of a rock crystal acquired by a Talbot-Lau grating interferometer X-ray computed tomography device, as well as a hyperspectral image. [ABSTRACT FROM AUTHOR]

Thirions Demons algorithm, first introduced in [5], is an intuitive and widely used matching algorithm. In contrast to other algorithms it is not obvious, that there is any reasonable energy which is minimized by the Demons algorithm. In [6] it is shown, that Thirions Demons algorithm minimizes the energy introduced in [3] (cf. (1)). In order to show this result the authors used Lie theory. In this note a simplified proof is given using standard calculus on vector spaces instead of Lie theory. [ABSTRACT FROM AUTHOR]

In this paper, we derive compact representations of the depth function for a triangle undergoing motion or defocus blur. Unlike a static primitive, where the depth function is planar, the depth function is a rational function in time and the lens parameters. Furthermore, we show how these compact depth functions can be used to design an efficient depth buffer compressor/decompressor, which significantly lowers total depth buffer bandwidth usage for a range of test scenes. In addition, our compressor/decompressor is simpler in the number of operations needed to execute, which makes our algorithm more amenable for hardware implementation than previous methods. [ABSTRACT FROM AUTHOR]

We introduce a bounding volume hierarchy based on the Slab Cut Ball. This novel type of enclosing shape provides an attractive balance between tightness of fit, cost of overlap testing, and memory requirement. The hierarchy construction algorithm includes a new method for the construction of tight bounding volumes in worst case time, which means our tree data structure is constructed in time using traditional top-down building methods. A fast overlap test method between two slab cut balls is also proposed, requiring as few as 28–99 arithmetic operations, including the transformation cost. Practical collision detection experiments confirm that our tree data structure is amenable for high performance collision queries. In all the tested benchmarks, our bounding volume hierarchy consistently gives performance improvements over the sphere tree, and it is also faster than the OBB tree in five out of six scenes. In particular, our method is asymptotically faster than the sphere tree, and it also outperforms the OBB tree, in close proximity situations. [ABSTRACT FROM AUTHOR]

The use of high dynamic range (HDR) textures in real-time graphics applications can increase realism and provide a more vivid experience. However, the increased bandwidth and storage requirements for uncompressed HDR data can become a major bottleneck. Hence, several recent algorithms for HDR texture compression have been proposed. In this paper, we discuss several practical issues one has to confront in order to develop and implement HDR texture compression schemes. These include improved texture filtering and efficient offline compression. For compression, we describe how Procrustes analysis can be used to quickly match a predefined template shape against chrominance data. To reduce the cost of HDR texture filtering, we perform filtering prior to the colour transformation, and use a simple trick to reduce the incurred errors. We also introduce a number of novel compression modes, which can be combined with existing compression schemes, or used on their own. [ABSTRACT FROM AUTHOR]