82,346 resources related to Visualization
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This symposium pertains to the field of electromagnetic compatibility.
All fields of satellite, airborne and ground remote sensing.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
All areas of ionizing radiation detection - detectors, signal processing, analysis of results, PET development, PET results, medical imaging using ionizing radiation
HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics. From specific algorithms to full system implementations, CG&A offers a strong combination of peer-reviewed feature articles and refereed departments, including news and product announcements. Special Applications sidebars relate research stories to commercial development. Cover stories focus on creative applications of the technology by an artist or ...
IEEE Transactions on Visualization and Computer Graphics, 2016
With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time ...
IEEE Transactions on Visualization and Computer Graphics, 2016
Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications it is necessary to compare tensor fields, e.g., to study the ...
IEEE Transactions on Visualization and Computer Graphics, 2006
This paper describes methods for explanatory and illustrative visualizations used to communicate aspects of Einstein's theories of special and general relativity, their geometric structure, and of the related fields of cosmology and astrophysics. Our illustrations target a general audience of laypersons interested in relativity. We discuss visualization strategies, motivated by physics education and the didactics of mathematics, and describe what ...
IEEE Transactions on Visualization and Computer Graphics, 2013
Analysis of multivariate data is of great importance in many scientific disciplines. However, visualization of 3D spatially-fixed multivariate volumetric data is a very challenging task. In this paper we present a method that allows simultaneous real-time visualization of multivariate data. We redistribute the opacity within a voxel to improve the readability of the color defined by a regular transfer function, ...
IEEE Transactions on Visualization and Computer Graphics, 2009
Time-varying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to explore data at different temporal resolutions to discover and highlight ...
High-Resolution Earthquake Simulations
Augmented Reality in Operating Rooms
Collection, Modeling & Interpretation of Mobile Sensor Big Data - Santosh Kumar - IEEE EMBS at NIH, 2019
Big Data Analytics: Tools and Technologies - Big Data Analytics Tutorial Part 2
ICASSP 2010 - Advances in Neural Engineering
IEEE Corporate Innovation Award - Pixar Animation Studios - 2018 IEEE Honors Ceremony
Q&A: Mixed Reality Panel - TTM 2018
Part 1: Mixed Reality Panel - TTM 2018
Localization Services for Online Common Operational Picture and Situation Awareness
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 4 of 7 - MRI at 130 Microtesla
Data Science in the Time of COVID-19 | IEEE TechEthics Virtual Panel
Bringing Biological Models to Life: The Power of Agent-based Modeling and Visualization
VisualDx Augmented Intelligence Project - Arthur Papier - IEEE EMBS at NIH, 2019
Part 2: Mixed Reality Panel - TTM 2018
Lloyd Watts: Reverse-Engineering the Human Auditory Pathway -WCCI 2012 Plenary talk
With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this paper, we put forward a model for incremental visualizations that is based on the established Data State Reference Model, but extends it in ways to also represent partitioned data and visualization operators to facilitate intermediate visualization updates. In combination, partitioned data and operators can be used independently and in combination to strike tailored compromises between output quality, shown data quantity, and responsiveness-i.e., frame rates. We showcase the new expressive power of this model by discussing the opportunities and challenges of incremental visualization in general and its usage in a real world scenario in particular.
Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications it is necessary to compare tensor fields, e.g., to study the effects of acquisition parameters, or to investigate the influence of pathologies on white matter structures. This comparison is commonly done by extracting scalar information out of the tensor fields and then comparing these scalar fields, which leads to a loss of information. If the glyph representation is kept, simple juxtaposition or superposition can be used. However, neither facilitates the identification and interpretation of the differences between the tensor fields. Inspired by the checkerboard style visualization and the superquadric tensor glyph, we design a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. Because tensor scale, anisotropy type, and orientation are related to anatomical information relevant for DTI applications, we focus on visualizing tensor differences in these three aspects. As demonstrated in a user study, our new glyph design allows users to efficiently and effectively identify the tensor differences. We also apply our new glyphs to investigate the differences between DTI datasets of the human brain in two different contexts using different b-values, and to compare datasets from a healthy and HIV-infected subject.
This paper describes methods for explanatory and illustrative visualizations used to communicate aspects of Einstein's theories of special and general relativity, their geometric structure, and of the related fields of cosmology and astrophysics. Our illustrations target a general audience of laypersons interested in relativity. We discuss visualization strategies, motivated by physics education and the didactics of mathematics, and describe what kind of visualization methods have proven to be useful for different types of media, such as still images in popular science magazines, film contributions to TV shows, oral presentations, or interactive museum installations. Our primary approach is to adopt an egocentric point of view: the recipients of a visualization participate in a visually enriched thought experiment that allows them to experience or explore a relativistic scenario. In addition, we often combine egocentric visualizations with more abstract illustrations based on an outside view in order to provide several presentations of the same phenomenon. Although our visualization tools often build upon existing methods and implementations, the underlying techniques have been improved by several novel technical contributions like image-based special relativistic rendering on GPUs, special relativistic 4D ray tracing for accelerating scene objects, an extension of general relativistic ray tracing to manifolds described by multiple charts, GPU-based interactive visualization of gravitational light deflection, as well as planetary terrain rendering. The usefulness and effectiveness of our visualizations are demonstrated by reporting on experiences with, and feedback from, recipients of visualizations and collaborators.
Analysis of multivariate data is of great importance in many scientific disciplines. However, visualization of 3D spatially-fixed multivariate volumetric data is a very challenging task. In this paper we present a method that allows simultaneous real-time visualization of multivariate data. We redistribute the opacity within a voxel to improve the readability of the color defined by a regular transfer function, and to maintain the see-through capabilities of volume rendering. We use predictable procedural noise - random-phase Gabor noise - to generate a high-frequency redistribution pattern and construct an opacity mapping function, which allows to partition the available space among the displayed data attributes. This mapping function is appropriately filtered to avoid aliasing, while maintaining transparent regions. We show the usefulness of our approach on various data sets and with different example applications. Furthermore, we evaluate our method by comparing it to other visualization techniques in a controlled user study. Overall, the results of our study indicate that users are much more accurate in determining exact data values with our novel 3D volume visualization method. Significantly lower error rates for reading data values and high subjective ranking of our method imply that it has a high chance of being adopted for the purpose of visualization of multivariate 3D data.
Time-varying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to explore data at different temporal resolutions to discover and highlight data based upon time-varying trends. Using the wavelet transform along the time axis, we transform data points into multi-scale time series curve sets. The time curves are clustered so that data of similar activity are grouped together, at different temporal resolutions. The data are displayed to the user in a global time view spreadsheet where she is able to select temporal clusters of data points, and filter and brush data across temporal scales. With our method, a user can interact with data based on time activities and create expressive visualizations.
Direct projection of 3D branching structures, such as networks of cables, blood vessels, or neurons onto a 2D image creates the illusion of intersecting structural parts and creates challenges for understanding and communication. We present a method for visualizing such structures, and demonstrate its utility in visualizing the abdominal aorta and its branches, whose tomographic images might be obtained by computed tomography or magnetic resonance angiography, in a single 2D stylistic image, without overlaps among branches. The visualization method, termed uncluttered single-image visualization (USIV), involves optimization of geometry. This paper proposes a novel optimization technique that utilizes an interesting connection of the optimization problem regarding USIV to the protein structure prediction problem. Adopting the integer linear programming-based formulation for the protein structure prediction problem, we tested the proposed technique using 30 visualizations produced from five patient scans with representative anatomical variants in the abdominal aortic vessel tree. The novel technique can exploit commodity-level parallelism, enabling use of general-purpose graphics processing unit (GPGPU) technology that yields a significant speedup. Comparison of the results with the other optimization technique previously reported elsewhere suggests that, in most aspects, the quality of the visualization is comparable to that of the previous one, with a significant gain in the computation time of the algorithm.
Volume illustration can be used to provide insight into source data from CT/MRI scanners in much the same way as medical illustration depicts the important details of anatomical structures. As such, proven techniques used in medical illustration should be transferable to volume illustration, providing scientists with new tools to visualize their data. In recent years, a number of techniques have been developed to enhance the rendering pipeline and create illustrative effects similar to the ones found in medical textbooks and surgery manuals. Such effects usually highlight important features of the subject while subjugating its context and providing depth cues for correct perception. Inspired by traditional visual and line-drawing techniques found in medical illustration, we have developed a collection of fast algorithms for more effective emphasis/de-emphasis of data as well as conveyance of spatial relationships. Our techniques utilize effective outlining techniques and selective depth enhancement to provide perceptual cues of object importance as well as spatial relationships in volumetric datasets. Moreover, we have used illustration principles to effectively combine and adapt basic techniques so that they work together to provide consistent visual information and a uniform style.
We evaluate and compare video visualization techniques based on fast-forward. A controlled laboratory user study (n = 24) was conducted to determine the trade-off between support of object identification and motion perception, two properties that have to be considered when choosing a particular fast-forward visualization. We compare four different visualizations: two representing the state-of-the-art and two new variants of visualization introduced in this paper. The two state-of-the-art methods we consider are frame-skipping and temporal blending of successive frames. Our object trail visualization leverages a combination of frame-skipping and temporal blending, whereas predictive trajectory visualization supports motion perception by augmenting the video frames with an arrow that indicates the future object trajectory. Our hypothesis was that each of the state-of-the-art methods satisfies just one of the goals: support of object identification or motion perception. Thus, they represent both ends of the visualization design. The key findings of the evaluation are that object trail visualization supports object identification, whereas predictive trajectory visualization is most useful for motion perception. However, frame-skipping surprisingly exhibits reasonable performance for both tasks. Furthermore, we evaluate the subjective performance of three different playback speed visualizations for adaptive fast-forward, a subdomain of video fast-forward.
Visualization applications nowadays not only face increasingly larger datasets, but have to solve increasingly complex research questions. They often require more than a single algorithm and consequently a software solution will exceed the possibilities of simple research prototypes. Well- established systems intended for such complex visual analysis purposes have usually been designed for classical, mesh-based graphics approaches. For particle-based data, however, existing visualization frameworks are too generic - e.g. lacking possibilities for consistent low-level GPU optimization for high-performance graphics - and at the same time are too limited - e.g. by enforcing the use of structures suboptimal for some computations. Thus, we developed the system softwareMegaMol for visualization research on particle- based data. On the one hand, flexible data structures and functional module design allow for easy adaption to changing research questions, e.g. studying vapors in thermodynamics, solid material in physics, or complex functional macromolecules like proteins in biochemistry. Therefore, MegaMol is designed as a development framework. On the other hand, common functionality for data handling and advanced rendering implementations are available and beneficial for all applications. We present several case studies of work implemented using our system as well as a comparison to other freely available or open source systems.
A comparative visualization of multiple volume data sets is challenging due to the inherent occlusion effects, yet it is important to effectively reveal uncertainties, correlations and reliable trends in 3D ensemble fields. In this paper we present bidirectional linking of multi-charts and volume visualization as a means to analyze visually 3D scalar ensemble fields at the data level. Multi-charts are an extension of conventional bar and line charts: They linearize the 3D data points along a space-filling curve and draw them as multiple charts in the same plot area. The bar charts encode statistical information on ensemble members, such as histograms and probability densities, and line charts are overlayed to allow comparing members against the ensemble. Alternative linearizations based on histogram similarities or ensemble variation allow clustering of spatial locations depending on data distribution. Multi-charts organize the data at multiple scales to quickly provide overviews and enable users to select regions exhibiting interesting behavior interactively. They are further put into a spatial context by allowing the user to brush or query value intervals and specific distributions, and to simultaneously visualize the corresponding spatial points via volume rendering. By providing a picking mechanism in 3D and instantly highlighting the corresponding data points in the chart, the user can go back and forth between the abstract and the 3D view to focus the analysis.
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