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.
Fig. 1. Starting from a single random point, our dynamic illustration method creates high-quality stipple drawings with a small number of iterations (left to right: 12, 14, and 18 iterations). In the example shown above we end up with 36k points of constant size.We propose an adaptive version of Lloyd's optimization method that distributes points based on Voronoi diagrams. Our inspiration is the LindeBuzo-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.
Left is much better Left is slightly better No difference Right is slightly better Right is much better Figure 1: Comparing the abstraction quality of two stipple illustrations (left ∼15k and right ∼4k points) to an input image taken from our user study. We use this relative information to derive values for the absolute perceived abstraction quality of stippled representations.
We propose a technique to represent two-dimensional data using stipples. While stippling is ollen 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 20 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 tech nique using datasets from different domains and conduct observation-validating studies to assess the perception of stippled representations.
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