99,811 research outputs found
3-D interactive visualisation tools for HI spectral line imaging
Upcoming HI surveys will deliver such large datasets that automated
processing using the full 3-D information to find and characterize HI objects
is unavoidable. Full 3-D visualization is an essential tool for enabling
qualitative and quantitative inspection and analysis of the 3-D data, which is
often complex in nature. Here we present , an open-source
extension of 3DSlicer, a multi-platform open source software package for
visualization and medical image processing, which we developed for the
inspection and analysis of HI spectral line data. We describe its initial
capabilities, including 3-D filtering, 3-D selection and comparative modelling
SlicerAstro: a 3-D interactive visual analytics tool for HI data
SKA precursors are capable of detecting hundreds of galaxies in HI in a
single 12 hours pointing. In deeper surveys one will probe more easily faint HI
structures, typically located in the vicinity of galaxies, such as tails,
filaments, and extraplanar gas. The importance of interactive visualization has
proven to be fundamental for the exploration of such data as it helps users to
receive immediate feedback when manipulating the data. We have developed
SlicerAstro, a 3-D interactive viewer with new analysis capabilities, based on
traditional 2-D input/output hardware. These capabilities enhance the data
inspection, allowing faster analysis of complex sources than with traditional
tools. SlicerAstro is an open-source extension of 3DSlicer, a multi-platform
open source software package for visualization and medical image processing.
We demonstrate the capabilities of the current stable binary release of
SlicerAstro, which offers the following features: i) handling of FITS files and
astronomical coordinate systems; ii) coupled 2-D/3-D visualization; iii)
interactive filtering; iv) interactive 3-D masking; v) and interactive 3-D
modeling. In addition, SlicerAstro has been designed with a strong, stable and
modular C++ core, and its classes are also accessible via Python scripting,
allowing great flexibility for user-customized visualization and analysis
tasks.Comment: 18 pages, 11 figures, Accepted by Astronomy and Computing.
SlicerAstro link: https://github.com/Punzo/SlicerAstro/wiki#get-slicerastr
What the eye does not see: visualizations strategies for the data collection of personal networks
The graphic representation of relational data is one of the central elements of social network analysis. In this paper, the author describe
the use of visualization in interview-based data collection procedures
designed to obtain personal networks information, exploring four
main contributions. First, the author shows a procedure by which the
visualization is integrated with traditional name generators to facilitate obtaining information and reducing the burden of the interview
process. Second, the author describes the reactions and qualitative
interpretation of the interviewees when they are presented with an
analytical visualization of their personal network. The most frequent
strategies consist in identifying the key individuals, dividing the personal network in groups and classifying alters in concentric circles
of relative importance. Next, the author explores how the visualization of groups in personal networks facilitates the enumeration of the
communities in which individuals participate. This allows the author
to reflect on the role of social circles in determining the structure of
personal networks. Finally, the author compares the graphic representation obtained through spontaneous, hand-drawn sociograms
with the analytical visualizations elicited through software tools. This
allows the author to demonstrate that analytical procedures reveal
aspects of the structure of personal networks that respondents are
not aware of, as well as the advantages and disadvantages of using
both modes of data collection. For this, the author presents findings
from a study of highly skilled migrants living in Spain (n = 95) through
which the author illustrates the challenges, in terms of data reliability,
validity and burden on both the researcher and the participants
The interaction of lean and building information modeling in construction
Lean construction and Building Information Modeling are quite different initiatives, but both are having profound impacts on the construction industry. A rigorous analysis of the myriad specific interactions between them indicates that a synergy exists which, if properly understood in theoretical terms, can be exploited to improve construction processes beyond the degree to which it might be improved by application of either of these paradigms independently. Using a matrix that juxtaposes BIM functionalities with prescriptive lean construction principles, fifty-six interactions have been identified, all but four of which represent constructive interaction. Although evidence for the majority of these has been found, the matrix is not considered complete, but rather a framework for research to
explore the degree of validity of the interactions. Construction executives, managers, designers and developers of IT systems for construction can also benefit from the framework as an aid to recognizing the potential synergies when planning their lean and BIM adoption strategies
Construction safety and digital design: a review
As digital technologies become widely used in designing buildings and infrastructure, questions arise about
their impacts on construction safety. This review explores relationships between construction safety and
digital design practices with the aim of fostering and directing further research. It surveys state-of-the-art
research on databases, virtual reality, geographic information systems, 4D CAD, building information
modeling and sensing technologies, finding various digital tools for addressing safety issues in the
construction phase, but few tools to support design for construction safety. It also considers a literature on
safety critical, digital and design practices that raises a general concern about ‘mindlessness’ in the use of
technologies, and has implications for the emerging research agenda around construction safety and digital
design. Bringing these strands of literature together suggests new kinds of interventions, such as the
development of tools and processes for using digital models to promote mindfulness through multi-party
collaboration on safet
Visual Integration of Data and Model Space in Ensemble Learning
Ensembles of classifier models typically deliver superior performance and can
outperform single classifier models given a dataset and classification task at
hand. However, the gain in performance comes together with the lack in
comprehensibility, posing a challenge to understand how each model affects the
classification outputs and where the errors come from. We propose a tight
visual integration of the data and the model space for exploring and combining
classifier models. We introduce a workflow that builds upon the visual
integration and enables the effective exploration of classification outputs and
models. We then present a use case in which we start with an ensemble
automatically selected by a standard ensemble selection algorithm, and show how
we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture
Fireground location understanding by semantic linking of visual objects and building information models
This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding
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