Marc Spicker

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.

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.

Woodification: User-Controlled Cambial Growth Modeling

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.

Weighted linde-buzo-gray stippling

We propose an adaptive version of Lloyds 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.

Interactive testbed for research in autism—the SARA project

The project Stylized Animations for Research on Autism (SARA) aims to develop a better understanding of the cognitive processes behind emotional categorization in children and adolescents with high-functioning autism spectrum disorder (ASD), in comparison with neurotypically developed peers. To this end, we combine novel real-time non-photorealistic rendering (NPR) algorithms, emotional facial animations, and eye-tracking technologies in a framework that serves as an interactive testbed for empirical research. In this paper, we focus on three experiments that: (1) validate real-time facial animations of virtual characters, (2) evaluate the NPR algorithms to create abstracted facial expressions, and (3) elucidate the relation between eye gaze behavior, ASD and alexithymia (i.e., difficulties in expressing ones emotions). The results show that our animations can be used in the proposed experiments; however, more evaluation is needed regarding the NPR abstractions, especially with individuals with ASD. Finally, even though no correlation was found between gaze behavior, ASD and alexithymia, the study opened several questions that will be addressed in future work.

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-Fechners 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.

Emotion recognition in autism spectrum disorder: does stylization help?

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.

Structure-aware Stylization of Mountainous Terrains.

We present a method for the stylization of mountainous terrains that allows creating abstract representations in different rendering styles. Our method consists of two major components: structure-aware terrain filtering and streamline-based hatching. For a given input terrain we compute different Levels-of-Detail (LoD) according to a crest line oriented importance measure and then filter each LoD accordingly. We generate flow fields for each LoD and compute streamlines to direct the production of hatching lines. The combination of crest and silhouette lines with streamline-based hatching allows us to create a variety of styles in different Levels-of-Detail. We evaluate our method using several terrains and demonstrate the effectiveness of our method by composing a number of different illustration styles.

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.

On the Trail of Facial Processing in Autism Spectrum Disorders

To investigate the difficulties in communication, socialization abilities, and emotion perception deficits in individuals with autism spectrum disorders (ASD), we propose the project SARA. Its main goal is to assess how abstracted emotional facial expressions influence the categorization of the emotions by children and adolescents with high-functioning ASD. This paper focuses on the first pilot study where an adapted version of the Dynamic Emotional Categorization Test (DECT) was implemented. The results support the validity of the emotional facial animations, which is confirmed by the low performance of subjects with high-functioning ASD when categorizing emotions, in comparison with their peers without ASD.