We investigate how the perceived abstraction quality of computer-generated illustrations is related to the number of primitives (points and small lines) used to create them. Since it is difficult to find objective functions that quantify the visual quality of such illustrations, we propose an approach to derive perceptual models from a user study. By gathering comparative data in a crowdsourcing user study and employing a paired comparison model, we can reconstruct absolute quality values. Based on an exemplary study for stippling, we show that it is possible to model the perceived quality of stippled representations based on the properties of an input image. The generalizability of our approach is demonstrated by comparing models for different stippling methods. By showing that our proposed approach also works for small lines, we demonstrate its applicability toward quantifying different representational drawing elements. Our results can be related to Weber--Fechner’s law from psychophysics and indicate a logarithmic relationship between number of rendering primitives in an illustration and the perceived abstraction quality thereof.

We propose a technique to represent two-dimensional data using stipples. While stippling is often regarded as an illustrative method, we argue that it is worth investigating its suitability for the visualization domain. For this purpose, we generalize the Linde-Buzo-Gray stippling algorithm for information visualization purposes to encode continuous and discrete 2D data. Our proposed modifications provide more control over the resulting distribution of stipples for encoding additional information into the representation, such as contours. We show different approaches to depict contours in stipple drawings based on locally adjusting the stipple distribution. Combining stipple-based gradients and contours allows for simultaneous assessment of the overall structure of the data while preserving important local details. We discuss the applicability of our technique using datasets from different domains and conduct observation-validating studies to assess the perception of stippled representations.

We propose an adaptive version of Lloyd's optimization method that distributes points based on Voronoi diagrams. Our inspiration is the Linde-Buzo-Gray-Algorithm in vector quantization, which dynamically splits Voronoi cells until a desired number of representative vectors is reached. We reformulate this algorithm by splitting and merging Voronoi cells based on their size, greyscale level, or variance of an underlying input image. The proposed method automatically adapts to various constraints and, in contrast to previous work, requires no good initial point distribution or prior knowledge about the final number of points. Compared to weighted Voronoi stippling the convergence rate is much higher and the spectral and spatial properties are superior. Further, because points are created based on local operations, coherent stipple animations can be produced. Our method is also able to produce good quality point sets in other fields, such as remeshing of geometry, based on local geometric features such as curvature.

We investigate the effect that stylized facial expressions have on the perception and categorization of emotions by participants with high-functioning Autism Spectrum Disorder (ASD) in contrast to two control samples: one with Attention-Deficit/Hyperactivity Disorder (ADHD), and one with neurotypically developed peers (NTD). Realtime Non-Photorealistic Rendering (NPR) techniques with different levels of abstraction are applied to stylize two animated virtual characters performing expressions for six basic emotions. Our results show that the accuracy rates of the ASD group were unaffected by the NPR styles and reached about the same performance as for the characters with realistic-looking appearance. This effect, however, was not seen in the ADHD and NTD groups.

Abstract We present a botanical simulation of secondary (cambial) tree growth coupled to a physical cracking simulation of its bark. Whereas level set growth would use a fixed resolution voxel grid, our system extends the deformable simplicial complex (DSC), supporting new biological growth functions robustly on any surface polygonal mesh with adaptive subdivision, collision detection and topological control. We extend the DSC with temporally coherent texturing, and surface cracking with a user‐controllable biological model coupled to the stresses introduced by the cambial growth model.

Abstract When representing 2D data points with spacious objects such as labels, overlap can occur. We present a simple algorithm which modifies the (Mani‐) Wordle idea with scan‐line based techniques to allow a better placement. We give an introduction to common placement techniques from different fields and compare our method to these techniques w.r.t. euclidean displacement, changes in orthogonal ordering as well as shape and size preservation. Especially in dense scenarios our method preserves the overall shape better than known techniques and allows a good trade‐off between the other measures. Applications on real world data are given and discussed.