Artistic Composition for Painterly Rendering

We present a technique for painterly renderings that follows a decomposition of the canvas into a set of regions and layers (coarse to fine). The regions reflect the spatial arrangement of the composition and the order in which the painting is to be created (typically back to front), and are produced in a way that new strokes only minimally paint over existing ones. Layers reflect the application of tools and are optimized for certain brush sizes. The number of strokes and colors that are needed to represent an input image are minimized by this decomposition, which is good for software, but essential for hardware-based rendering. Our method allows us to apply different painting styles to different regions as well as layers, and to create painterly renderings with more artistic freedom. We demonstrate our decomposition technique on images that are processed using hierarchical segmentation techniques.publishe

Weighted linde-buzo-gray stippling

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.publishe

Interactive Visualization of Complex Plant Ecosystems

We present a method for interactive rendering of large outdoor scenes. Complex polygonal plant models and whole plant populations are represented by relatively small sets of point and line primitives.This enables us to show landscapes faithfully using only a limited percentage of primitives. In addition, a hierarchical data structure allows us to smoothly reduce the geometrical representation to any desired number of primitives. The scene is hierarchically divided into local portions of geometry to achieve large reduction factors for distant regions. Additionally, the data reduction is adapted to the visual importance of geometric objects. This allows us to maintain the visual fidelity of the representation while reducing most of the geometry drastically. With our system, we are able to interactively render very complex landscapes with good visual quality

Depth-aware coherent line drawings

In this paper we utilize depth information to extend a line drawing algorithm, improving depth perception and object differentiation in large and spatially complex scenes. We consider different scales of features and apply a flow-based morphological filter to the scenes. Based on this two line drawing styles are defined. The proposed algorithm works in real-time and enables users to manipulate the parameter space through instant visual feedback. We evaluated the effectiveness of our method by performing a user study.publishe

Stippling of 2D Scalar Fields

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.publishe

Rolled-out wordles : a heuristic method for overlap removal of 2D data representatives

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

Quantifying visual abstraction quality for stipple drawings

We investigate how the perceived abstraction quality of stipple illustrations is related to the number of points used to create them. Since it is difficult to find objective functions that quantify the visual quality of such illustrations, we gather comparative data by a crowdsourcing user study and employ a paired comparison model to deduce absolute quality values. Based on this study we show that it is possible to predict the perceived quality of stippled representations based on the properties of an input image. Our results are related to Weber-Fechner's law from psychophysics and indicate a logarithmic relation between numbers of points and perceived abstraction quality. We give guidance for the number of stipple points that is typically enough to represent an input image well.publishe

Quantifying Visual Abstraction Quality for Computer-Generated Illustrations

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.publishe