The IEEE Scientific Visualization (SciVis) conference solicits novel research ideas and innovative applications in all areas of scientific visualization. The scope of the conference, co-located at VIS with the annual IEEE Visual Analytics and IEEE Information Visualization Conferences, includes both fundamental research contributions within scientific visualization, as well as advances toward understanding or solving real world problems, or that impact a particular application in a significant way.
For the first time, IEEE SciVis 2018 will feature a separate short papers track with a submission deadline of June 13, 2018, i.e., after notifications for full papers have been sent out. This is the call for participation for IEEE SciVis short papers.
This call for participation is for regular full paper submissions (with abstract deadline of March 21, 2018, and paper deadline of March 31, 2018)
Authors are invited to submit original work presenting fundamental research, practice and experience, or novel applications, in all areas of scientific visualization and related topics.
Suggested topics include, but are not limited to:
Visualization, rendering, and manipulation of spatial data: scalar, vector, and tensor fields; multidimensional, multi-field, multi-modal, and multivariate data; time-varying data; regular and unstructured grids; point-based data; and volumetric data.
Foundations: visualization taxonomies and models; mathematical theories for visualization; perception (theory, color, texture, scene, motion); cognition; aesthetics; information theoretic approaches; knowledge-assisted visualization; presentation; production; dissemination; and visual design.
Systems and methodologies: system and toolkit design; topology-based and geometry-based techniques; feature extraction and pattern analysis; glyph-based, texture-based, pixel-oriented techniques; uncertainty, view-dependent, and illustrative visualization; visual storytelling; computational steering; sonification; collaborative and distributed visualization; and integrating spatial and non-spatial data visualization.
Large data visualization: parallel, distributed, cluster, and grid computing; high-performance computing on multi-core, GPUs, FPGA, and embedded devices; petascale and exascale visualization; scalability; visualization over networks; compression; multi-resolution techniques; and streaming data.
Data science: scalable data management on and off the cloud; storage and data analytics; information extraction and knowledge discovery from big data; statistical modeling; data mining; machine learning, including deep learning; clustering techniques; application of computer vision techniques; and visual steering for data retrieval.
Interaction: human-computer interaction for visualization; interaction design; coordinated multiple views; brushing & linking; focus & context; zooming and navigation; data editing, manipulation, and deformation; guided visualization; multimodal input devices; haptics for visualization; mobile and ubiquitous visualization; and interaction with visualizations in different display environments.
Display techniques: large and high-resolution displays; gigapixel displays; small displays; mobile devices; wrist and wearable displays; stereo displays; immersive and virtual environments; mixed and augmented visualization; and projector-camera systems.
Evaluation and user studies: task and requirements analysis; metrics and benchmarks; qualitative evaluation; quantitative evaluation; laboratory studies; eye tracking studies and studies with other physiological sensors; field studies; usability studies; design studies; validation and verification; crowdsourcing; and human computation.
Application areas of visualization: mathematics; physical sciences and engineering; earth, space, and environmental sciences; urban science; business and finance; social and information sciences; education; humanities; multimedia (image/video/music); robotics; sensor networks; cybersecurity; visualization for visualization research; visualization for the masses; terrain visualization; geographic/geospatial visualization; software visualization; bioinformatics; and molecular, biomedical, and medical visualization.
Please note that papers that focus on visual analytics might be a better match for the IEEE VAST Conference at IEEE VIS. Similarly, papers that focus on information visualization might be a better match for the IEEE InfoVis Conference, also at IEEE VIS. The papers co-chairs reserve the right to move papers between these three conferences based on topic and perceived fit.
A technique paper describes a new or significantly improved algorithm or technique in sufficient detail so that other researchers can reproduce the results. This technique should ideally be of general application rather than being restricted to a single task or single source of data, and the exposition should be focused on what the technique does, how it does it, when to use it, and what the computational and other costs are.
A system paper describes a solution to a problem where the major task is building a large complex software artifact, applying largely known visualization techniques. Here, the focus should be on the design decisions, the implications for software / hardware structure, and comparison with other systems.
An application paper normally starts with an encapsulated description of a problem domain and the questions to be resolved by visualization, then describes the application of visualization to the task, any novel techniques developed, and how the visualization solution answered the questions posed. Techniques related to a single problem are normally application papers, and evaluation is often limited because many application papers are essentially custom software for a specific problem.
An evaluation paper is usually an empirical assessment of how effective a technique or system is when used by humans. As such, these often involve rigorous experimental protocols and statistical analysis, but this is not the only possible form of evaluation. Good evaluation papers go beyond statistical analysis to explain causes, construct models and predict effectiveness of related systems.
A theory paper describes aspects of the process by which humans construct visualizations to explore data or communicate with other humans. These papers do not usually involve implementation, but contribute by illuminating the role of visualization in data analysis and often by proposing models for improving visualization as a discipline.
Email: scivis_papers@ieeevis.org.
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