KOUŘIL, David, Ladislav ČMOLÍK, Barbora KOZLÍKOVÁ, Hsiang-Yun WU, Graham JOHNSON, David S. GOODSELL, Arthur OLSON, Eduard M. GROELLER and Ivan VIOLA. Labels on Levels: Labeling of Multi-Scale Multi-Instance and Crowded 3D Biological Environments. IEEE Transactions on Visualization and Computer Graphics. 2019, vol. 25, No 1, p. 977-986. ISSN 1077-2626. Available from: https://dx.doi.org/10.1109/TVCG.2018.2864491.
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Basic information
Original name Labels on Levels: Labeling of Multi-Scale Multi-Instance and Crowded 3D Biological Environments
Authors KOUŘIL, David (203 Czech Republic), Ladislav ČMOLÍK (203 Czech Republic), Barbora KOZLÍKOVÁ (203 Czech Republic, belonging to the institution), Hsiang-Yun WU, Graham JOHNSON (840 United States of America), David S. GOODSELL (840 United States of America), Arthur OLSON (840 United States of America), Eduard M. GROELLER (40 Austria) and Ivan VIOLA (703 Slovakia, guarantor).
Edition IEEE Transactions on Visualization and Computer Graphics, 2019, 1077-2626.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10200 1.2 Computer and information sciences
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 4.558
RIV identification code RIV/00216224:14330/19:00108862
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1109/TVCG.2018.2864491
UT WoS 000452640000093
Keywords in English labeling;multi-scale;multi-scale;molecular visualization
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 13/4/2020 23:04.
Abstract
Labeling is intrinsically important for exploring and understanding complex environments and models in a variety of domains. We present a method for interactive labeling of crowded 3D scenes containing very many instances of objects spanning multiple scales in size. In contrast to previous labeling methods, we target cases where many instances of dozens of types are present and where the hierarchical structure of the objects in the scene presents an opportunity to choose the most suitable level for each placed label. Our solution builds on and goes beyond labeling techniques in medical 3D visualization, cartography, and biological illustrations from books and prints. In contrast to these techniques, the main characteristics of our new technique are: 1) a novel way of labeling objects as part of a bigger structure when appropriate, 2) visual clutter reduction by labeling only representative instances for each type of an object, and a strategy of selecting those. The appropriate level of label is chosen by analyzing the scene's depth buffer and the scene objects' hierarchy tree. We address the topic of communicating the parent-children relationship between labels by employing visual hierarchy concepts adapted from graphic design. Selecting representative instances considers several criteria tailored to the character of the data and is combined with a greedy optimization approach. We demonstrate the usage of our method with models from mesoscale biology where these two characteristics—multi-scale and multi-instance—are abundant, along with the fact that these scenes are extraordinarily dense.
Links
MUNI/M/0822/2015, interní kód MUName: Expressive Visualization of Protein Complexes
Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects
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