79 research outputs found

    The State-of-the-Art of Set Visualization

    Get PDF
    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

    Get PDF
    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

    Full text link
    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

    Get PDF
    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

    Get PDF
    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

    Full text link
    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
    corecore