The Making of Continuous Colormaps

Nardini, Pascal; Chen, Min; Samsel, Francesca; Bujack, Roxana; Böttinger, Michael; Scheuermann, Gerik

doi:10.1109/tvcg.2019.2961674

Cited by 24 publications

(14 citation statements)

References 87 publications

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“…When choosing the colours for the visualisations, we focused on colour palettes that are colourblind friendly and considered the basic properties of human visual perception, taking into account the work of Brewer et al (2022), Okabe and Ito (2008) and Nardini et al (2021). Only for the visualisation of the stratigraphic layers (see Figure 2) was this not possible due to the large number of categories and the fact that the colours for the different rock types are predefined in the lithostratigraphic lexicon of Switzerland 21 provided by the Federal Office of Topography (swisstopo) and the Swiss Academy of Sciences.…”

Section: Accessibilitymentioning

confidence: 99%

Prototype of a Virtual Experiment Information System for the Mont Terri Underground Research Laboratory

Graebling

Şen²,

Bilke³

et al. 2022

Front. Earth Sci.

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Underground Research Laboratories (URLs) allow geoscientific in-situ experiments at large scale. At the Mont Terri URL in Switzerland, international research groups conduct numerous experiments in parallel. The measured and simulated data as well as research results obtained from them are highly relevant as they improve the general understanding of geological processes, for example in the context of radioactive waste disposal. Unfortunately, the data obtained at the test site is often only available to researchers who are directly involved in a particular experiment. Furthermore, typical visualisation techniques of such data by domain scientists often lack spatial context and accessing and exploring the data requires prior technical knowledge and a high level of effort. We created a digital replica of the Mont Terri URL and thereby implemented a prototype of a Virtual Experiment Information System that integrates highly heterogeneous data from several different sources. It allows accessing and exploring the relevant data embedded in its spatial context without much prior technical knowledge. Both, simulation results and observation data are displayed within the same system. The 4D visualisation approach focuses on three exemplary experiments conducted at Mont Terri and is easily transferable to other experiments or even other URLs. The Unity Game Engine has been used to develop the prototype. This allowed to build the application for various output devices like desktop computers or Virtual Reality hardware without much additional effort. The implemented system reduces the technical effort required to access and explore highly relevant research data and lowers the cognitive effort usually needed to gain insights from measurements, simulation models and context data. Moreover, it promotes exchange among research groups by enabling interactive visualisations embedded in the URL’s spatial context. In addition, a future use of the system for the communication of scientific methods and results to stakeholders or the general public is plausible.

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Section: Accessibilitymentioning

confidence: 99%

Prototype of a Virtual Experiment Information System for the Mont Terri Underground Research Laboratory

Graebling

Şen²,

Bilke³

et al. 2022

Front. Earth Sci.

Self Cite

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“…Last, our network learns distinct features among color histograms by consuming vast amount of visualizations and corresponding colormaps. We would like to further enrich the training dataset by considering treestructured colormaps [45], semantic colormaps [27], and user-created continuous colormaps [33], and add some noise to the synthetic visualizations to better cope with real-world visualizations. Nevertheless, a more appropriate approach would be to directly learn color design rules.…”

Section: Discussionmentioning

confidence: 99%

Deep Colormap Extraction From Visualizations

Wen

Zeng

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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This work presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of ∼64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous, and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases, i.e., color transfer and color remapping.

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“…For example, In the graph theory, the transitivity factor of a graph [4,38] is based on the relative number of triangles in the graph, compared to total number of connected triples of nodes, which indicates any transitive nodes of a graph can be represented by the observed extreme nodes with the corresponding transitivity factor. Besides, transition states also exist in other fields of area, such as color gradients model [27] in computer graphics, phyletic gradualism [29] in evolutionary biology, state vector [9,10] in the theorem of Schrödinger picture and etc.…”

Section: Preliminary Definition Of Transitivitymentioning

confidence: 99%

Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution

Huang¹,

Li²,

Hu³

et al. 2021

Preprint

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Being extremely dependent on the iterative estimation and correction of data or models, the existing blind superresolution (SR) methods are generally time-consuming and less effective. To address it, this paper proposes a transitive learning method for blind SR using an end-to-end network without any additional iterations in inference. To begin with, we analyze and demonstrate the transitivity of degradations, including the widely used additive and convolutive degradations. We then propose a novel Transitive Learning method for blind Super-Resolution on transitive degradations (TLSR), by adaptively inferring a transitive transformation function to solve the unknown degradations without any iterative operations in inference. Specifically, the end-to-end TLSR network consists of a degree of transitivity (DoT) estimation network, a homogeneous feature extraction network, and a transitive learning module. Quantitative and qualitative evaluations on blind SR tasks demonstrate that the proposed TLSR achieves superior performance and consumes less time against the stateof-the-art blind SR methods. The code is available at github.com/YuanfeiHuang/TLSR.

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The Making of Continuous Colormaps

Cited by 24 publications

References 87 publications

Prototype of a Virtual Experiment Information System for the Mont Terri Underground Research Laboratory

Prototype of a Virtual Experiment Information System for the Mont Terri Underground Research Laboratory

Deep Colormap Extraction From Visualizations

Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution

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