Section: New Results

Expressive Rendering and Visualization

Participants : Pierre Bénard, Georges-Pierre Bonneau, Alexandre Coninx, Joëlle Thollot.

Temporal Coherence for Stylized Animation

Participants : Pierre Bénard, Joëlle Thollot.

Figure 8. In our state-of-the-art report we review and carefully compare Temporal Coherence techniques for stylized animations.

Non-photorealistic rendering (NPR) algorithms allow the creation of images in a variety of styles, ranging from line drawing and pen-and-ink to oil painting and watercolor. These algorithms provide greater flexibility, control and automation over traditional drawing and painting. Despite significant progress over the past 15 years, the application of NPR to the generation of stylized animations remains an active area of research. The main challenge of computer generated stylized animations is to reproduce the look of traditional drawings and paintings while minimizing distracting flickering and sliding artifacts present in hand-drawn animations. These goals are inherently conflicting and any attempt to address the temporal coherence of stylized animations is a trade-off. We have published the state-of-the-art report [15] motivated by the growing number of methods proposed in recent years and the need for a comprehensive analysis of the trade-offs they propose. We formalize the problem of temporal coherence in terms of goals and compare existing methods accordingly. We propose an analysis for both line and region stylization methods and discuss initial steps toward their perceptual evaluation. The goal of our report is to help uninformed readers to choose the method that best suits their needs, as well as motivate further research to address the limitations of existing methods.

Visualization of data with uncertainty using perceptually guided procedural noise

Participants : Alexandre Coninx, Georges-Pierre Bonneau.

Figure 9. Left: classical colormap visualization of scalar data without uncertainty. Right: in our technique, we pertub the input of the colormap using a perceptually guided procedural noise, scaled by the uncertainty of the data. The data and its uncertainty can be visualized in the same image.

This work is the result of a collaboration with EdF R&D and Jacques Droulez, Director of Research at CNRS in Collège de France. In his PhD work, Alexandre Coninx has introduced a new method to visualize uncertain scalar data fields by combining color scale visualization techniques with animated, perceptually adapted Perlin noise. The parameters of the Perlin noise are controlled by the uncertainty information to produce animated patterns showing local data value and quality, as illustrated in Figure 9 . In order to precisely control the perception of the noise patterns, we perform a psychophysical evaluation of contrast sensitivity thresholds for a set of Perlin noise stimuli. We validate and extend this evaluation using an existing computational model. This allows us to predict the perception of the uncertainty noise patterns for arbitrary choices of parameters. We demonstrate and discuss the efficiency and the benefits of our method with various settings, color maps and data sets. This work has been published at APGV'2011 [21] .