Browsing by Author "Ballester-Ripoll, Rafael"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    SenVis: Interactive Tensor-based Sensitivity Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Yang, Haiyan; Ballester-Ripoll, Rafael; Pajarola, Renato; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Sobol's method is one of the most powerful and widely used frameworks for global sensitivity analysis, and it maps every possible combination of input variables to an associated Sobol index. However, these indices are often challenging to analyze in depth, due in part to the lack of suitable, flexible enough, and fast-to-query data access structures as well as visualization techniques. We propose a visualization tool that leverages tensor decomposition, a compressed data format that can quickly and approximately answer sophisticated queries over exponential-sized sets of Sobol indices. This way, we are able to capture the complete global sensitivity information of high-dimensional scalar models. Our application is based on a three-stage visualization, to which variables to be analyzed can be added or removed interactively. It includes a novel hourglass-like diagram presenting the relative importance for any single variable or combination of input variables with respect to any composition of the rest of the input variables. We showcase our visualization with a range of example models, whereby we demonstrate the high expressive power and analytical capability made possible with the proposed method.
  • Thumbnail Image
    Item
    VIAN: A Visual Annotation Tool for Film Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Halter, Gaudenz; Ballester-Ripoll, Rafael; Flueckiger, Barbara; Pajarola, Renato; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    While color plays a fundamental role in film design and production, existing solutions for film analysis in the digital humanities address perceptual and spatial color information only tangentially. We introduce VIAN, a visual film annotation system centered on the semantic aspects of film color analysis. The tool enables expert-assessed labeling, curation, visualization and classification of color features based on their perceived context and aesthetic quality. It is the first of its kind that incorporates foreground-background information made possible by modern deep learning segmentation methods. The proposed tool seamlessly integrates a multimedia data management system, so that films can undergo a full color-oriented analysis pipeline.