EuroVisShort2020

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https://diglib.eg.org/handle/10.2312/2632897

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The Effect of Graph Layout on the Perception of Graph Properties
(The Eurographics Association, 2020) Kypridemou, Elektra; Zito, Michele; Bertamini, Marco; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
The way in which a graph is described visually is crucial for the understanding and analysis of its structure. In this study we explore how different drawing layouts affect our perception of the graph's properties. We study the perception of connectedness, tree-ness and density using four different layouts: the Circular, Grid, Planar and Spring layouts. Results show that some layouts are better than others when we need to decide whether a graph is a tree or is connected. More sophisticated algorithms, like Planar and Spring, facilitate our perception, while Circular and Grid layouts lead to performance not better than chance. However, when perceiving the density of a graph, no layout was found to be better than the others.
Effective Visualization of Sparse Image-to-Image Correspondences
(The Eurographics Association, 2020) Andujar, Carlos; Chica, Antonio; Comino Trinidad, Marc; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Finding robust correspondences between images is a crucial step in photogrammetry applications. The traditional approach to visualize sparse matches between two images is to place them side-by-side and draw link segments connecting pixels with matching features. In this paper we present new visualization techniques for sparse correspondences between image pairs. Key ingredients of our techniques include (a) the clustering of consistent matches, (b) the optimization of the image layout to minimize occlusions due to the super-imposed links, (c) a color mapping to minimize color interference among links (d) a criterion for giving visibility priority to isolated links, (e) the bending of link segments to put apart nearby links, and (f) the use of glyphs to facilitate the identification of matching keypoints. We show that our technique substantially reduces the clutter in the final composite image and thus makes it easier to detect and inspect both inlier and outlier matches. Potential applications include the validation of image pairs in difficult setups and the visual comparison of feature detection / matching algorithms.
Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques
(The Eurographics Association, 2020) Bors, Christian; Eichner, Christian; Miksch, Silvia; Tominski, Christian; Schumann, Heidrun; Gschwandtner, Theresia; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Time series segmentation is employed in various domains and continues to be a relevant topic of research. A segmentation pipeline is composed of different steps involving several parameterizable algorithms. Existing Visual Analytics approaches can help experts determine appropriate parameterizations and corresponding segmentation results for a given dataset. However, the results may also be afflicted with different types of uncertainties. Hence, experts face the additional challenge of understanding the reliability of multiple alternative the segmentation results. So far, the influence of uncertainties in the context of time series segmentation could not be investigated. We present an uncertainty-aware exploration approach for analyzing large sets of multivariate time series segmentations. The approach features an overview of uncertainties and time series segmentations, while detailed exploration is facilitated by (1) a lens-based focus+context technique and (2) uncertainty-based re-arrangement. The suitability of our uncertainty-aware design was evaluated in a quantitative user study, which resulted in interesting findings of general validity.
Fast Design Space Rendering of Scatterplots
(The Eurographics Association, 2020) Santala, Simo; Oulasvirta, Antti; Weinkauf, Tino; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
The design space of scatterplots consists of a number of parameters such as marker size and shape, image width and aspect ratio, and opacity. Different parameters yield different visual impressions of the scatterplot. Perceptual optimization of scatterplots means finding the best design parameters to support a given visualization task. This requires rendering thousands of design variations. We describe an image-based method for rendering scatterplots, which is tailored to this scenario: it enables quick updates of the design by re-using previously calculated intermediate results, and is independent of the data set size. Our approach outperforms the classic method of rendering scatterplots, i.e., drawing each marker individually onto an image, and can therefore dramatically speed up the perceptual optimization of scatterplots. We provide an open-source implementation and an online service for our method.
GaCoVi: a Correlation Visualization to Support Interpretability-Aware Feature Selection for Regression Models
(The Eurographics Association, 2020) Rojo, Diego; Htun, Nyi Nyi; Verbert, Katrien; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
The recent growth of interest in explainable artificial intelligence (XAI) has resulted in a large number of research efforts to provide accountable and transparent machine learning systems. Although a large volume of research has focused on algorithm transparency, there are other factors that influence the interpretability of a system, such as end-users' understanding of individual features and the total number of features. Thus, involving end-users in the feature selection process may be key to achieving interpretability. In addition, previous work has suggested that to obtain satisfactory interpretability and predictive performance, the feature selection process should look for a subset of features that are highly correlated with the response variable yet uncorrelated to each other. Taking this into account, in this paper, we present a work-in-progress design study of a novel system for correlation visualization, GaCoVi. GaCoVi is designed to put domain experts in the loop of feature selection for regression models in scenarios where transparency of the machine learning systems is crucial.
Joint-Sphere: Intuitive and Detailed Human Joint Motion Representation
(The Eurographics Association, 2020) Kim, Seonghun; Balasubramanyam, Adithya; Kim, Dubeom; Chai, Young Ho; Patil, Ashok Kumar; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
A motion comparison method using images allows for the motions to be easily recognized and to see differences between each action. However, when using images, orientation differences between similar motions cannot be quantified. Although many studies have been conducted on methods to represent the data and apply detailed motion comparisons, these representations are difficult to understand because the relationship between the motion and the representation is not clear. This paper introduces a novel motion representation method called the Joint-Sphere that enables detailed motion comparisons and an intuitive understanding of each joint movement. In each Joint-Sphere, the movement of a specific joint part is represented. Several Joint- Spheres can be used to represent a full-body motion. The results from a dance motion pattern show that each joint movement can be compared accurately even when several joints are moving quickly.
Progressive Rendering of Transparent Integral Surfaces
(The Eurographics Association, 2020) Tian, Xingze; Günther, Tobias; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Integral surfaces are a useful method in illustrative and geometry-based flow visualization, as they convey shading, depth and geometric information better than their line-based counterparts. However, they are not as frequently used as line-based techniques due to the added complexity that arises from their computation. Frontline-based methods, such as stream surfaces and path surfaces require an adaptive subdivision of the frontline, whereas advected surfaces, such as streak surfaces and time surfaces, require refinement and possibly retriangulation of the entire surface after each time step. In this paper, we extend an image-space surface rendering technique to support transparency, which enables the application of illustrative surface rendering techniques without the need to adaptively refine frontlines or entire surfaces. We develop a pixel-based dynamic tree data structure that is progressively filled with integral curves and compactly stores the transparent layers arising in the rendering of the surfaces. We apply the method to the illustrative rendering of path surfaces and streak surfaces in a number of time-dependent vector fields.
Sketchy Rendering to Aid the Recollection of Regular Visualizations
(The Eurographics Association, 2020) Larsen, Michael Reidun Engelbrecht; Han, Wenkai; Schulz, Hans-Jörg; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Some visualizations have a more regular visual appearance than others. For example, while stream graphs or force-directed network layouts feature a unique, almost organic 'look and feel', matrices or unit treemaps can become rather bland, grid-like visualizations in which one data item is hard to tell apart from the next. In this paper, we investigate the use of sketchy rendering for such grid-like visualizations to give them a slightly more unique 'look and feel' themselves. We evaluate our approach in a lab study (N = 16) where participants were asked to re-find a given grid cell in regular and sketchy grids. We find that users who make conscious use of the sketchy features can benefit from certain forms of sketchy rendering in terms of task completion times.

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Browsing EuroVisShort2020 by Subject "Computing methodologies"

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