Interactive 3-D Visualization: A tool for seafloor navigation, exploration, and engineering

Recent years have seen remarkable advances in sonar technology, positioning capabilities, and computer processing power that have revolutionized the way we image the seafloor. The massive amounts of data produced by these systems present many challenges but also offer tremendous opportunities in terms of visualization and analysis. We have developed a suite of interactive 3-D visualization and exploration tools specifically designed to facilitate the interpretation and analysis of very large (10\u27s to 100\u27s of megabytes), complex, multi-component spatial data sets. If properly georeferenced and treated, these complex data sets can be presented in a natural and intuitive manner that allows the integration of multiple components each at their inherent level of resolution and without compromising the quantitative nature of the data. Artificial sun-illumination, shading, and 3-D rendering can be used with digital bathymetric data (DTM\u27s) to form natural looking and easily interpretable, yet quantitative, landscapes. Color can be used to represent depth or other parameters (like backscatter or sediment properties) which can be draped over the DTM, or high resolution imagery can be texture mapped on bathymetric data. When combined with interactive analytical tools, this environment has facilitated the use of multibeam sonar and other data sets in a range of geologic, environmental, fisheries, and engineering applications

inPHAP: Interactive visualization of genotype and phased haplotype data

Background: To understand individual genomes it is necessary to look at the variations that lead to changes in phenotype and possibly to disease. However, genotype information alone is often not sufficient and additional knowledge regarding the phase of the variation is needed to make correct interpretations. Interactive visualizations, that allow the user to explore the data in various ways, can be of great assistance in the process of making well informed decisions. But, currently there is a lack for visualizations that are able to deal with phased haplotype data. Results: We present inPHAP, an interactive visualization tool for genotype and phased haplotype data. inPHAP features a variety of interaction possibilities such as zooming, sorting, filtering and aggregation of rows in order to explore patterns hidden in large genetic data sets. As a proof of concept, we apply inPHAP to the phased haplotype data set of Phase 1 of the 1000 Genomes Project. Thereby, inPHAP's ability to show genetic variations on the population as well as on the individuals level is demonstrated for several disease related loci. Conclusions: As of today, inPHAP is the only visual analytical tool that allows the user to explore unphased and phased haplotype data interactively. Due to its highly scalable design, inPHAP can be applied to large datasets with up to 100 GB of data, enabling users to visualize even large scale input data. inPHAP closes the gap between common visualization tools for unphased genotype data and introduces several new features, such as the visualization of phased data.Comment: BioVis 2014 conferenc

Experimenter's Laboratory for Visualized Interactive Science

ELVIS (Experimenter's Laboratory for Visualized Interactive Science) is an interactive visualization environment that enables scientists, students, and educators to visualize and analyze large, complex, and diverse sets of scientific data. It accomplishes this by presenting the data sets as 2-D, 3-D, color, stereo, and graphic images with movable and multiple light sources combined with displays of solid-surface, contours, wire-frame, and transparency. By simultaneously rendering diverse data sets acquired from multiple sources, formats, and resolutions and by interacting with the data through an intuitive, direct-manipulation interface, ELVIS provides an interactive and responsive environment for exploratory data analysis

Developing Interaction 3D Models for E-Learning Applications

Some issues concerning the development of interactive 3D models for e-learning applications are considered. Given that 3D data sets are normally large and interactive display demands high performance computation, a natural solution would be placing the computational burden on the client machine rather than on the server. Mozilla and Google opted for a combination of client-side languages, JavaScript and OpenGL, to handle 3D graphics in a web browser (Mozilla 3D and O3D respectively). Based on the O3D model, core web technologies are considered and an example of the full process involving the generation of a 3D model and their interactive visualization in a web browser is described. The challenging issue of creating realistic 3D models of objects in the real world is discussed and a method based on line projection for fast 3D reconstruction is presented. The generated model is then visualized in a web browser. The experiments demonstrate that visualization of 3D data in a web browser can provide quality user experience. Moreover, the development of web applications are facilitated by O3D JavaScript extension allowing web designers to focus on 3D contents generation

Visualizing association rules in hierarchical groups

Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Although visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming, most association rule visualization techniques are still falling short when it comes to large numbers of rules. In this paper we introduce a new interactive visualization method, the grouped matrix representation, which allows to intuitively explore and interpret highly complex scenarios. We demonstrate how the method can be used to analyze large sets of association rules using the R software for statistical computing, and provide examples from the implementation in the R-package arulesViz. (authors' abstract

Semi-supervised construction of general visualization hierarchies

We have recently developed a principled approach to interactive non-linear hierarchical visualization [8] based on the Generative Topographic Mapping (GTM). Hierarchical plots are needed when a single visualization plot is not sufficient (e.g. when dealing with large quantities of data). In this paper we extend our system by giving the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in [8], whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of GTMs is used. The latter is particularly useful when the plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a data set of 2300 18-dimensional points and mention extension of our system to accommodate discrete data types

Massive model visualization: An investigation into spatial partitioning

The current generation of visualization software is incapable of handling the interactive rendering of arbitrarily large models. While many solutions have been proposed for Massive Model Visualization, very few are able to achieve the full capabilities needed for a computer visualization solution. In most cases this is due to overly complex approaches that, while achieving impressive frame rates, make it virtually impossible to implement features like part manipulation. What is needed is a simple approach with rendering performance bounded by screen complexity not model size, with primitive traceability to the original model to facilitate part manipulation, and capability to be modified in near-real-time. This thesis introduces MMDr, a simple system to achieve interactive frame rates on extremely large data sets, while retaining support for most if not all the features required for a computer visualization solution

Software for signal processing and display of large 3-D data sets

We have recently developed a software system, called DETECT/NDE, which provides a powerful environment for processing and visualizing large ultrasonic data sets. This system runs on X-Windows UNIX workstations and on VAX/VMS computers, and combines interactive graphics-based visualization with signal processing algorithms. The two main features of this package are: (1) the interactive generation and display of B-scans and C-scans from the 3-D data sets; and (2) the provision of high resolution deconvolution and inversion procedures for improving the interpretability of the data. In addition, numerous other utilities allow image processing, animation and 3-D rendering of the data

Interactive exploration and modeling of large data sets: a case study with Venus light scattering data

We present a system where visualization and the control of the simulation are integrated to facilitate interactive exploration and modeling of large data sets. The system was developed to estimate properties of the atmosphere of Venus from comparison between measured and simulated data. Reuse of results, distributed computing, and multiple views on the data were the major ingredients to create an effective environment