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  • Eleanor Lutz: Making Art From Public Data
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2020-01-07
    Eleanor Lutz; Annie Bares

    We asked Eleanor Lutz for an interview upon investigating work she has shared through her Tabletop Whale blog (tabletopwhale.com/). On her blog, Eleanor shares her love of design as applied to the visualization of public datasets. She provides examples and the processes by which her stunning graphics come to fruition, as well as code examples in a notebook format that are useful as a service to the graphics community and her potential collaborators. As we prepared for the interview, we became more and more enamored with her Atlas of Space visualizations—many of which she designed specifically to be printed as wall posters. In this article, we share the insights she shared with us during the interview.

    更新日期:2020-01-10
  • Origins of Global Illumination
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2020-01-07
    Turner Whitted

    Global illumination refers to a complete shading model that simulates real lighting and reflection as accurately as possible. Whether used for product prototyping or special effects for entertainment, the goal is to match the appearance of the real world. The origins of global illumination come at the intersection of a steady progression of shading models with the ancient simulation technique of ray tracing.

    更新日期:2020-01-10
  • Towards Placental Surface Vasculature Exploration in Virtual Reality.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : null
    Johannes Novotny,Wesley R Miller,Francois I Luks,Derek Merck,Scott Collins,David H Laidlaw

    We present a case study evaluating the potential for interactively identifying placental surface blood vessels using magnetic resonance imaging (MRI) scans in virtual reality (VR) environments. We visualized the MRI data using direct volume rendering in a high-fidelity CAVE-like VR system, allowing medical professionals to identify relevant placental vessels directly from volume visualizations in the VR system, without prior vessel segmentation. Participants were able to trace most of the observable vascular structure, and consistently identified blood vessels down to diameters of 1 mm, an important requirement in diagnosing vascular diseases. Qualitative feedback from our participants suggests that our VR visualization is easy to understand and allows intuitive data exploration, but complex user interactions remained a challenge. Using these observations, we discuss implications and requirements for spatial tracing user interaction methods in VR environments. We believe that VR MRI visualizations are the next step towards effective surgery planning for prenatal diseases.

    更新日期:2020-01-10
  • A Compelling Virtual Tour of the Dunhuang Cave with Immersive Head-Mounted Display.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-09-05
    Ping-Hsuan Han,Yang-Sheng Chen,Iou-Shiuan Liu,Yu-Ping Jang,Ling Tsai,Alvin Chang,Yi-Ping Hung

    Dunhuang Caves are home to the largest Buddhist art sites in the world and are listed as a UNESCO World Heritage Site. Over time, the murals have been damaged by both humans and nature. In this paper, we present an immersive virtual reality system for exploring spatial cultural heritage, which utilize the digitized data from the DunHuang Research Academy to represent the virtual environment of the cave. In this system, the interaction techniques that allow users to flexibly experience any of the artifacts or displays contribute to their understanding of the cultural heritage. Additionally, we evaluated the system by conducting a user study to examine the extent of user acquaintance after the entire experience. Our result has shown what participants learn from the spatial context and augmented information in the VR. This can be used as design considerations for developing other spatial heritages.

    更新日期:2020-01-10
  • Aggregated Ensemble Views for Deep Water Asteroid Impacts Simulations.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-05-10
    Simon Leistikow,Karim Huesmann,Alexey Fofonov,Lars Linsen

    Simulation ensembles such as the ones simulating deep water asteroid impacts have many facets. Their analysis in terms of detecting spatio-temporal patterns, comparing multiple runs, and analyzing the influence of simulation parameters requires aggregation at multiple levels. We propose respective visual encodings embedded in an interactive visual analysis tool.

    更新日期:2020-01-10
  • Under Water to Outer Space: Augmented Reality for Astronauts and Beyond
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2020-01-07
    Benjamin Nuernberger; Robert Tapella; Samuel-Hunter Berndt; So Young Kim; Sasha Samochina

    Augmented reality (AR) has the potential to help astronauts execute procedures in a quicker, more intuitive, and safer way. A key part of realizing these benefits has been the use of an undersea research facility—the Aquarius—that acts as an analog to the International Space Station to a certain extent. In a June 2019 mission, the Aquarius crew successfully executed a complex procedure taking place across four different task areas by using an AR application called ProtoSpace developed at the Jet Propulsion Laboratory. In this article, we share the detailed results of the study, lessons learned, and future work needed to further enable the enhancement of procedure execution through augmented reality.

    更新日期:2020-01-10
  • Enabling Domain Expertise in Scientific Visualization With CinemaScience
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2020-01-07
    Terece L. Turton; Divya Banesh; Trinity Overmyer; Benjamin H. Sims; David H. Rogers

    Scientific users present unique challenges to visualization researchers. Their high-level tasks require them to apply domain-specific expertise. We introduce a broader audience to the CinemaScience project and demonstrate how CinemaScience enables efficient visualization workflows that can bring in scientist expertise and drive scientific insight.

    更新日期:2020-01-10
  • Automatic Generation of Typographic Font from Small Font Subset.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-08-06
    Tomo Miyazaki,Tatsunori Tsuchiya,Yoshihiro Sugaya,Shinichiro Omachi,Masakazu Iwamura,Seiichi Uchida,Koichi Kise

    The automated generation of fonts containing a large number of characters is in high demand. For example, a typical Japanese font requires over 1,000 characters. Unfortunately, professional typographers create the majority of fonts, resulting in significant financial and time investments for font generation. The main contribution of this paper is the development of a method that automatically generates a target typographic font containing thousands of characters, from a small subset of character images in the target font. We generate characters other than the subset so that a complete font is obtained. We propose a novel font generation method with the capability to deal with various fonts, including a font composed of distinctive strokes, which are difficult for existing methods to handle. We demonstrated the proposed method by generating 2,965 characters in 47 fonts. Moreover, objective and subjective evaluations verified that the generated characters are similar to the original characters.

    更新日期:2020-01-10
  • Interactive Video Completion.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-02
    Makoto Okabe,Keita Noda,Yoshinori Dobashi,Ken Anjyo

    We propose an interactive video completion method aiming for practical use in a digital production workplace. The results of earlier automatic solutions often require considerable amount of manual modifications to make them usable in practice. To reduce such a laborious task, our method offers an efficient editing tool. Our iterative algorithm estimates the flow fields and colors in space-time holes in the video. As in earlier approaches, our algorithm uses an L1 data term to estimate flow fields. However, we employ a novel L2 data term to estimate temporally coherent color transitions. Our GPU implementation enables the user to interactively complete a video by drawing holes and immediately removes objects from the video. In addition, our method successfully interpolates sparse modifications initialized by the designer. According to our subjective evaluation, the videos completed with our method look significantly better than those with other state-of-the-art approaches.

    更新日期:2020-01-10
  • Stroke-based sketched symbol reconstruction and segmentation.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-10-04
    Kurmanbek Kaiyrbekov,Metin Sezgin

    Hand-drawn objects usually consist of multiple semantically meaningful parts. In this paper, we propose a neural network model that segments sketched symbols into stroke-level components. Our segmentation framework has two main elements: a fixed feature extractor and a Multilayer Perceptron (MLP) network that identifies a component based on the feature. As the feature extractor we utilize an encoder of a stroke-rnn, which is our newly proposed generative Variational Auto-Encoder (VAE) model that reconstructs symbols on a stroke by stroke basis. Experiments show that a single encoder could be reused for segmenting multiple categories of sketched symbols with negligible effects on segmentation accuracies. Our segmentation scores surpass existing methodologies on an available small state of the art dataset. Moreover, extensive evaluations on our newly annotated big dataset demonstrate that our framework obtains significantly better accuracies as compared to baseline models. We release the dataset to the community.

    更新日期:2020-01-10
  • Turning a Smartphone Selfie Into a Studio Portrait
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2020-01-07
    Nicola Capece; Francesco Banterle; Paolo Cignoni; Fabio Ganovelli; Ugo Erra

    We introduce a novel algorithm that turns a flash selfie taken with a smartphone into a studio-like photograph with uniform lighting. Our method uses a convolutional neural network trained on a set of pairs of photographs acquired in a controlled environment. For each pair, we have one photograph of a subject's face taken with the camera flash enabled and another one of the same subject in the same pose illuminated using a photographic studio-lighting setup. We show how our method can amend lighting artifacts introduced by a close-up camera flash, such as specular highlights, shadows, and skin shine.

    更新日期:2020-01-10
  • [Front cover]
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Presents the front cover for this issue of the publication.

    更新日期:2020-01-04
  • Call for Papers: IEEE Transactions on Computers
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Provides a listing of current staff, committee members and society officers.

    更新日期:2020-01-04
  • IEEE Computer Society Has You Covered!
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Advertisement, IEEE.

    更新日期:2020-01-04
  • Table of contents
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Presents the table of contents for this issue of this publication.

    更新日期:2020-01-04
  • Masthead
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Provides a listing of current staff, committee members and society officers.

    更新日期:2020-01-04
  • In Time With Data
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-05
    Gary Singh

    Time-based media in the digital realm gave Morgan Barnard access to what eluded him in the analog world: the modular transformation from one milieu to another. Data could be used as a control source, whether it was MIDI data or atmospheric data.

    更新日期:2020-01-04
  • Visualization 4.0
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-05

    With this article, the author would like to provide a renewed perspective on opportunities for visualization and visual analytics in business. The thoughts presented are based on his experiences as both a visual analytics scholar and practitioner. He highlights research opportunities that have received limited attention or are just emerging but will undoubtedly play a very promising role in the near future. Broadly considered, he considers opportunities along four dimensions: (1) Making Sense of the Complex, (2) Accelerating the Provision, (3) Amplifying the Experience, and (4) Ensuring Integrity. The author fully acknowledges that what is presented is not comprehensive, many other promising topics likely exist, but hopes that the discussion will serve as an exciting call for action and stimulate the visualization community to pursue them actively.

    更新日期:2020-01-04
  • Security & Privacy
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

    更新日期:2020-01-04
  • Understanding Failure Mode Effect Analysis Data Using Interactive Visual Analytics
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-05
    Rahul C. Basole; Ahsan Qamar; Biswajyoti Pal; Michael Corral; Matthew Meinhart; Arpit Narechania

    Providing actionable insights through interactive visual analytics is essential to effective decision making. Yet, many complex systems engineering (SE) domains still lack such tools. Design reviews are often still based on static snapshots of data, without any dynamic interaction, data curation, and view creation capabilities to answer salient analysis questions. In this study, we report on a tool called DataHawk that helps answer common questions associated with one prominent SE context, namely failure mode and effect analysis (FMEA). The tool provides powerful exploration capabilities that enable system engineers, designers, and managers to probe FMEA data from multiple starting points, build questions dynamically, and find triangulated answers using multiple views rapidly. Field results are illustrated through a usage scenario from the automotive industry and show that the tool demonstrates the needed versatility, scalability, and effectiveness for real-world engineering data.

    更新日期:2020-01-04
  • Provenance Analysis for Sensemaking
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01
    Jean-Daniel Fekete; T. J. Jankun-Kelly; Melanie Tory; Kai Xu

    The articles in this special section examine the concept of "sensemaking", which refers to how we structure the unknown so as to be able to act in it. In the context of data analysis it involves understanding the data, generating hypotheses, selecting analysis methods, creating novel solutions, and critical thinking and learning wherever needed. Due to its explorative and creative nature, sensemaking is arguably the most challenging part of any data analysis.

    更新日期:2020-01-04
  • IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

    更新日期:2020-01-04
  • Analytic Provenance in Practice: The Role of Provenance in Real-World Visualization and Data Analysis Environments
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-08-05
    Karthic Madanagopal; Eric D. Ragan; Perakath Benjamin

    Practical data analysis scenarios involve more than just the interpretation of data through visual and algorithmic analysis. Many real-world analysis environments involve multiple types of experts and analysts working together to solve problems and make decisions, adding organizational and social requirements to the mix. We aim to provide new knowledge about the role of provenance for practical problems in a variety of analysis scenarios central to national security. We present the findings from interviews with data analysts from domains, such as intelligence analysis, cyber-security, and geospatial intelligence. In addition to covering multiple analysis domains, our study also considers practical workplace implications related to organizational roles and the level of analyst experience. The results demonstrate how different needs for provenance depend on different roles in the analysis effort (e.g., data analyst, task managers, data analyst trainers, and quality control analysts). By considering the core challenges reported along with an analysis of existing provenance-support techniques through existing research and systems, we contribute new insights about needs and opportunities for improvements to provenance-support methods.

    更新日期:2020-01-04
  • Apple Inc.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Advertisement.

    更新日期:2020-01-04
  • A Provenance Task Abstraction Framework
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-10-10
    Christian Bors; John Wenskovitch; Michelle Dowling; Simon Attfield; Leilani Battle; Alex Endert; Olga Kulyk; Robert S. Laramee

    Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance. We propose a task abstraction framework that consists of a three stage approach, composed of 1) initializing a provenance task hierarchy, 2) parsing the provenance hierarchy by using an abstraction mapping mechanism, and 3) leveraging the task hierarchy in an analytical tool. Furthermore, we identify implications to accommodate iterative refinement, context, variability, and uncertainty during all stages of the framework. We describe a use case which exemplifies our abstraction framework, demonstrating how context can influence the provenance hierarchy to support analysis. The article concludes with an agenda, raising and discussing challenges that need to be considered for successfully implementing such a framework.

    更新日期:2020-01-04
  • Capturing and Visualizing Provenance From Data Wrangling
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-09-18
    Christian Bors; Theresia Gschwandtner; Silvia Miksch

    Data quality management and assessment play a vital role for ensuring the trust in the data and its fitness-of-use for subsequent analysis. The transformation history of a data wrangling system is often insufficient for determining the usability of a dataset, lacking information how changes affected the dataset. Capturing workflow provenance along the wrangling process and combining it with descriptive information as data provenance can enable users to comprehend how these changes affected the dataset, and if they benefited data quality. We present DQProv Explorer, a system that captures and visualizes provenance from data wrangling operations. It features three visualization components: allowing the user to explore the provenance graph of operations and the data stream, the development of quality over time for a sequence of wrangling operations applied to the dataset, and the distribution of issues across the entirety of the dataset to determine error patterns.

    更新日期:2020-01-04
  • In Situ Visualization for Computational Science
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-05
    Hank Childs; Janine Bennett; Christoph Garth; Bernd Hentschel

    In situ visualization is an increasingly important approach for computational science, as it can address limitations on leading edge high-performance computers and also can provide an increased spatio-temporal resolution. However, there are many open research issues with effective in situ processing. This article describes the challenges identified by a recent Dagstuhl Seminar on the topic.

    更新日期:2020-01-04
  • Call for Articles
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

    更新日期:2020-01-04
  • Hierarchical Image Semantics Using Probabilistic Path Propagations for Biomedical Research
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-01-18
    Christina Gillmann; Tobias Post; Thomas Wischgoll; Hans Hagen; Ross Maciejewski

    Image segmentation is an important subtask in biomedical research applications, such as estimating the position and shape of a tumor. Unfortunately, advanced image segmentation methods are not widely applied in research applications as they often miss features, such as uncertainty communication, and may lack an intuitive approach for the use of the underlying algorithm. To solve this problem, this paper fuses a fuzzy and a hierarchical segmentation approach together, thus providing a flexible multiclass segmentation method based on probabilistic path propagations. By utilizing this method, analysts and physicians can map their mental model of image components and their composition to higher level objects. The probabilistic segmentation of higher order components is propagated along the user-defined hierarchy to highlight the potential of improvement resulting in each level of hierarchy by providing an intuitive representation. The effectiveness of this approach is demonstrated by evaluating our segmentations of biomedical datasets, comparing it to the state-of-the-art segmentation approaches, and an extensive user study.

    更新日期:2020-01-04
  • Morgan Barnard: Melding Our Environment and the Unseen Supplied Via Data
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-05
    Morgan Barnard; Bruce Donald Campbell

    Morgan Barnard is a digital artist and designer working in the areas of public art, immersive installations, and live cinema. His work offers audiences unique moments of observation aand reflection by creating work that combines lighting, projection, and interactivity with publicly available data sets. As a digital artist, Barnard aims to create data visualizations that are in collaboration with our environment to convey larger scientific ideas. With a background as a digital artist, filmmaker, motion graphics artist and educator, Morgan brings a broad multidisciplinary skill set to the projects he works on.

    更新日期:2020-01-04
  • Poor Man's Virtual Camera: Real-Time Simultaneous Matting and Camera Pose Estimation
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2016-03-18
    István Szentandrási; Markéta Dubská; Michal Zachariáš; Adam Herout

    Today's film and advertisement production heavily uses computer graphics combined with living actors by chroma keying. The matchmoving process typically takes a considerable manual effort. Semiautomatic matchmoving tools exist as well, but they still work offline and require manual check-up and correction. In this paper, we propose an instant matchmoving solution for green screen. It uses a recent technique of planar uniform marker fields. Our technique can be used in indie and professional filmmaking as a cheap and ultramobile virtual camera, and for shot prototyping and storyboard creation. The matchmoving technique based on marker fields of shades of green is very computationally efficient: we developed and present in this paper a mobile application running at 33 FPS. Our technique is thus available to anyone with a smartphone at low cost and with an easy setup, opening space for new levels of filmmakers' creative expression.

    更新日期:2020-01-04
  • Erratum
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    In the article “Challenges in Visual Analysis of Ensembles” by Patricia Crossno in the March/April 2018 issue of CG&A, several low-resolution images were published due to a production error. The corrected high-resolution images are displayed.

    更新日期:2020-01-04
  • IEEE Computer Society
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

    更新日期:2020-01-04
  • SHARE AND MANAGE YOUR RESEARCH DATA
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

    更新日期:2020-01-04
  • ComputingEdge
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Advertisement.

    更新日期:2020-01-04
  • Keep Your Career Options Open
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2019-11-01

    Advertisement.

    更新日期:2020-01-04
  • Visual Computing and the Progress of Developing Countries.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2016-07-13
    Alberto Raposo,Soraia Raupp Musse,James Gain

    更新日期:2019-11-01
  • Human Touch in Digital Experiences.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2016-05-18
    L Miguel Encarnação

    更新日期:2019-11-01
  • Human-Centered Data Visualization.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2016-03-01
    Xiaoru Yuan,Baoquan Chen,Koji Koyamada,Issei Fujishiro

    更新日期:2019-11-01
  • Virtual reality software and technology.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2015-12-22
    Taku Komura,Rynson W H Lau,Ming C Lin,Aditi Majumder,Dinesh Manocha,Wei Wei Xu

    更新日期:2019-11-01
  • Imagining Macondo: Interacting with García Márquez’s Literary Landscape.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2015-12-22
    Angus Graeme Forbes,Andres Burbano,Paul Murray,George Legrady

    Imagining Macondo is a public artwork that commemorates Nobel Prize-winning author Gabriel García Márquez. It was first showcased at the Bogota International Book Fair in April 2015 to an audience of more than 300,000 over the course of two weeks. The project involved extensive collaboration between an international team of artists, designers, and programmers. This article explores the historical and artistic contexts for the creation of the work, discusses the audience reception to the work, and describes the significant software and production requirements necessary to create an installation with thousands of participants and hundreds of thousands of viewers.

    更新日期:2019-11-01
  • 更新日期:2019-11-01
  • Natural User Interfaces for Adjustable Autonomy in Robot Control.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2015-07-28
    Joseph J LaViola,Odest Chadwicke Jenkins

    更新日期:2019-11-01
  • Interacting with Diverse Realities.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2015-07-28
    L Miguel Encarnação

    更新日期:2019-11-01
  • 更新日期:2019-11-01
  • In memoriam: Wolfgang Straßer (1941-2015).
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2015-05-16
    L Miguel Encarnação

    更新日期:2019-11-01
  • The next big thing.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2014-12-31
    Céline Loscos,Holly Rushmeier

    更新日期:2019-11-01
  • Visualizing 20 years of applications.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2014-12-31
    Mike Potel,Pak Chung Wong

    更新日期:2019-11-01
  • Virtual reality for the masses.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2014-11-08
    Lisa Avila,Mike Bailey

    更新日期:2019-11-01
  • The eventual triumph of art.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2014-11-08
    Gary Singh

    更新日期:2019-11-01
  • 更新日期:2019-11-01
  • Business intelligence analytics.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2014-11-08
    Danyel Fisher,Steven Drucker,Mary Czerwinski

    更新日期:2019-11-01
  • Behind the scenes.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2014-11-08
    L Miguel Encarnação

    更新日期:2019-11-01
  • Digital-content authoring.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2011-11-01
    Takeo Igarashi,Radomir Mech

    更新日期:2019-11-01
  • 2010 IEEE Visualization Contest Winner: interactive planning for brain tumor resections.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2011-09-01
    Stefan Diepenbrock,Jorg-Stefan Prassni,Florian Lindemann,Hans-Werner Bothe,Timo Ropinski

    更新日期:2019-11-01
  • Computer graphics: from desktop to mobile and web.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2011-07-01
    Jordi Linares-Pellicer,Pau Mico,Javier Esparza-Peidro,Empar Carrasquer-Moya

    更新日期:2019-11-01
  • Diving into the flow.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2011-07-01
    Gary Singh

    更新日期:2019-11-01
  • Encouraging the use of visualization.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2011-03-01
    L Miguel Encarnacao

    更新日期:2019-11-01
  • An infinite bag of tricks.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2011-03-01
    Gary Singh

    更新日期:2019-11-01
  • Against the grain.
    IEEE Comput. Graph. Appl. (IF 1.725) Pub Date : 2011-01-01
    Gary Singh

    更新日期:2019-11-01
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