Colloquy Cycle on Friday, November 16, 2018 - 10:30
Current Visual Analytics Research at CGV@TU Graz
Visual Analytics aims to support data analysis and exploration using interactive data visualization, tightly coupled with automatic data analysis methods. In this talk, we will introduce recent research in Visual Analytics at the Institute of Computer Graphics and Knowledge Visualization at TU Graz. After a brief introduction, we will first present approaches for visual similarity search and regression modeling in time series and scatter plot data, based on user sketches and lenses. Then, we will present approaches for visual analysis of movement data in team sports based on suitable visual data abstractions. In a third part, we will comment on recent research interest for guidance in visual data analysis, and describe our first ideas based on user eye tracking and relevance feedback. A summary concludes the talk.
Elastic Flattening of Painted Pottery Surfaces
Generating flat images from paintings on curved surfaces is an important task in Archaeological analysis of ancient pottery. It allows comparing styles and painting techniques, e.g, for style and workshop attribution, and serves as basis for domain publications which typically use 2d images. To obtain such flat images from scanned textured 3d models of the pottery objects, current practice is to perform so-called rollouts using approximating shape primitives like cones or spheres, onto which the mesh surfaces are projected. While this process provides in intuitive deformation metaphor for the users, it naturally introduces unwanted distortions in the mapping of the surface, especially for vessels with high-curvature profiles. In this work, we perform an elastic flattening of these projected meshes, where stretch energy is minimized by simulating a physical relaxation process on a damped elastic spring model. We propose an intuitive contraction-directed physical setup which allows for an efficient relaxation while ensuring a controlled convergence. Our work has shown to produce images of significantly improved suitability for domain experts’ tasks like interpretation, documentation and attribution of ancient pottery.