EG2018

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https://diglib.eg.org/handle/10.2312/2632158

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Browsing EG2018 by Subject "centered computing"

Now showing 1 - 4 of 4
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    Human Sensitivity to Light Zones in Virtual Scenes
    (The Eurographics Association, 2018) Kartashova, Tatiana; Ridder, Huib de; Pas, Susan F. te; Pont, Sylvia C.; Jain, Eakta and Kosinka, Jirí
    We investigated perception of light properties in scenes containing volumes with dramatically different light properties (direction, intensity, diffuseness). Each scene had two light zones, defined as distinct spatial groupings of lighting variables significant to the space- and form-giving characteristics of light [Mad07]. The results show that human observers are more sensitive to differences in illumination between two parts of a scene when the differences occur in the picture plane than in depth of the scene. We discuss implications for and possible applications of our results in computer graphics.
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    Image-Based Information Visualization Tutorial
    (The Eurographics Association, 2018) Hurter, Christophe; Ritschel, Tobias and Telea, Alexandru
    While many data exploration techniques are based on automatic knowledge extraction, other tools exist where the user plays the central role. This tutorial will report actual use-cases where the user interactively explores datasets and extracts relevant information. These techniques must be interactive enough to insure flexibility data exploration, therefore image-based algorithms propose a suitable solution. These algorithms, processed in parallel by the graphic card, are fast and scalable enough to support interactive big data exploration requirements.
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    Incorporating Visualization Research in Introductory Programming Course: Case Studies
    (The Eurographics Association, 2018) Kim, Sunghee; Post, Frits and Žára, Jirí
    The importance of early research experience for undergraduate students has been stressed time and time again. This paper presents three case studies in which non-CS major students could gain a visualization research experience in their first programming course. In all case studies, students were given real climate data to visualize. In the first case study, students visualized spatial correlation between two variables (weather conditions) on a map so that viewers could infer areas in which the two variables were highly correlated in a positive or negative way, or areas with little to no correlation. In the second and third case studies, students generated single variable visualization and multidimensional visualization of two or four variables. In each of the three case studies the students were led through the process of understanding data, exploring different representations, and designing and implementing an agreed-upon visual representation. Increased number of students decided to take the next course in Computer Science compared to previous years without a research project. Feedback from the students suggests that they enjoyed using data they could understand and found the process and the final product rewarding and applicable to projects in their major and courses.
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    Kinder-Gator: The UF Kinect Database of Child and Adult Motion
    (The Eurographics Association, 2018) Aloba, Aishat; Flores, Gianne; Woodward, Julia; Shaw, Alex; Castonguay, Amanda; Cuba, Isabella; Dong, Yuzhu; Jain, Eakta; Anthony, Lisa; Diamanti, Olga and Vaxman, Amir
    Research has suggested that children's whole-body motions are different from those of adults. However, research on children's motions, and how these motions differ from those of adults, is limited. One possible reason for this limited research is that there are few motion capture (mocap) datasets for children, with most datasets focusing on adults instead. There are even fewer datasets that have both children's and adults' motions to allow for comparison between them. To address these problems, we present Kinder-Gator, a new dataset of ten children and ten adults performing whole-body motions in front of the Kinect v1.0. The data contains RGB and 3D joint positions for 58 motions, such as wave, walk in place, kick, and point, which have been manually labeled according to the category of the participant (child vs. adult), and the motion being performed. We believe this dataset will be useful in supporting research and applications in animation and whole-body motion recognition and interaction.