79 research outputs found
The State-of-the-Art of Set Visualization
Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net
Splatting multiresolution volume data using the feature graph
We propose to represent classified datasets as a feature graph storing different graphical models and attributes for each feature. This graph allows us to render each feature according to its own characteristics. In addition, we show that various features of the graph storing volume information at different resolution levels can be rendered together using a view-aligned splatting method. Moreover, we propose a 2D kernel function for splats that is easy to tune and generates smaller footprints that reduce the render time. Our algorithm provides images with less blur. It enhances the boundary of the features while avoiding the subdivision of homogeneous regions of the volume.Postprint (published version
Scale-Space Splatting: Reforming Spacetime for the Cross-Scale Exploration of Integral Measures in Molecular Dynamics
Understanding large amounts of spatiotemporal data from particle-based
simulations, such as molecular dynamics, often relies on the computation and
analysis of aggregate measures. These, however, by virtue of aggregation, hide
structural information about the space/time localization of the studied
phenomena. This leads to degenerate cases where the measures fail to capture
distinct behaviour. In order to drill into these aggregate values, we propose a
multi-scale visual exploration technique. Our novel representation, based on
partial domain aggregation, enables the construction of a continuous
scale-space for discrete datasets and the simultaneous exploration of scales in
both space and time. We link these two scale-spaces in a scale-space space-time
cube and model linked views as orthogonal slices through this cube, thus
enabling the rapid identification of spatio-temporal patterns at multiple
scales. To demonstrate the effectiveness of our approach, we showcase an
advanced exploration of a protein-ligand simulation.Comment: 11 pages, 9 figures, IEEE SciVis 201
Ground truth determination for segmentation of tomographic volumes using interpolation
Dissertação para obtenção do Grau de Mestre em
Engenharia BiomédicaOptical projection tomographic microscopy allows for a 3D analysis of individual
cells, making it possible to study its morphology. The 3D imagining technique
used in this thesis uses white light excitation to image stained cells, and is
referred to as single-cell optical computed tomography (cell CT).
Studies have shown that morphological characteristics of the cell and its
nucleus are deterministic in cancer diagnoses. For a more complete and accurate analysis of these characteristics, a fully-automated analysis of the single-cell 3D tomographic images can be done. The first step is segmenting the image into the different cell components. To assess how accurate the segmentation is, there is a need to determine ground truth of the automated segmentation.
This dissertation intends to expose a method of obtaining ground truth for 3D segmentation of single cells. This was achieved by developing a software in CSharp.
The software allows the user to input a visual segmentation of each 2D slice of a 3D volume by using a pen to trace the visually identified boundary of a cell component on a tablet. With this information, the software creates a segmentation of a 3D tomographic image that is a result of human visual
segmentation.
To increase the speed of this process, interpolation algorithms can be used.
Since it is very time consuming to draw on every slice the user can skip slices.
Interpolation algorithms are used to interpolate on the skipped slices.
Five different interpolation algorithms were written: Linear Interpolation, Gaussian splat, Marching Cubes, Unorganized Points, and Delaunay Triangulation. To evaluate the performance of each interpolation algorithm the following evaluation metrics were used: Jaccard Similarity, Dice Coefficient,
Specificity and Sensitivity.After evaluating each interpolation method we concluded that linear interpolation was the most accurate interpolation method, producing the best
segmented volume for a faster ground truth determination method
Haptic Interaction with 3D oriented point clouds on the GPU
Real-time point-based rendering and interaction with virtual objects is gaining popularity
and importance as di�erent haptic devices and technologies increasingly provide the basis
for realistic interaction. Haptic Interaction is being used for a wide range of applications
such as medical training, remote robot operators, tactile displays and video games. Virtual
object visualization and interaction using haptic devices is the main focus; this process
involves several steps such as: Data Acquisition, Graphic Rendering, Haptic Interaction
and Data Modi�cation. This work presents a framework for Haptic Interaction using the
GPU as a hardware accelerator, and includes an approach for enabling the modi�cation
of data during interaction. The results demonstrate the limits and capabilities of these
techniques in the context of volume rendering for haptic applications. Also, the use
of dynamic parallelism as a technique to scale the number of threads needed from the
accelerator according to the interaction requirements is studied allowing the editing of
data sets of up to one million points at interactive haptic frame rates
Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs
Annual recruitment data of new graduates are manually analyzed by human
resources specialists (HR) in industries, which signifies the need to evaluate
the recruitment strategy of HR specialists. Every year, different applicants
send in job applications to companies. The relationships between applicants'
attributes (e.g., English skill or academic credential) can be used to analyze
the changes in recruitment trends across multiple years' data. However, most
attributes are unnormalized and thus require thorough preprocessing. Such
unnormalized data hinder the effective comparison of the relationship between
applicants in the early stage of data analysis. Thus, a visual exploration
system is highly needed to gain insight from the overview of the relationship
between applicants across multiple years. In this study, we propose the
Polarizing Attributes for Network Analysis of Correlation on Entities
Association (Panacea) visualization system. The proposed system integrates a
time-varying graph model and dynamic graph visualization for heterogeneous
tabular data. Using this system, human resource specialists can interactively
inspect the relationships between two attributes of prospective employees
across multiple years. Further, we demonstrate the usability of Panacea with
representative examples for finding hidden trends in real-world datasets and
then describe HR specialists' feedback obtained throughout Panacea's
development. The proposed Panacea system enables HR specialists to visually
explore the annual recruitment of new graduates
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