Transfer function design based on user selected samples for intuitive multivariate volume exploration

pre-printMultivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make multivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing features of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to automatically and robustly generate high dimensional transfer functions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimensionality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets

Interactive rendering and efficient querying for large multivariate seismic volumes on consumer level PCs

pre-printWe present a volume visualization method that allows interactive rendering and efficient querying of large multivariate seismic volume data on consumer level PCs. The volume rendering pipeline utilizes a virtual memory structure that supports out-of-core mul- tivariate multi-resolution data and a GPU-based ray caster that allows interactive multivariate transfer function design. A Gaussian mixture model representation is precomputed and nearly interactive querying is achieved by testing the Gaussian functions against user defined transfer functions on the GPU in the runtime. Finally, the method has been tested on a multivariate 3D seismic dataset which is larger than the size of the main memory of the testing machine

Doctor of Philosophy

dissertationVisualization and exploration of volumetric datasets has been an active area of research for over two decades. During this period, volumetric datasets used by domain users have evolved from univariate to multivariate. The volume datasets are typically explored and classified via transfer function design and visualized using direct volume rendering. To improve classification results and to enable the exploration of multivariate volume datasets, multivariate transfer functions emerge. In this dissertation, we describe our research on multivariate transfer function design. To improve the classification of univariate volumes, various one-dimensional (1D) or two-dimensional (2D) transfer function spaces have been proposed; however, these methods work on only some datasets. We propose a novel transfer function method that provides better classifications by combining different transfer function spaces. Methods have been proposed for exploring multivariate simulations; however, these approaches are not suitable for complex real-world datasets and may be unintuitive for domain users. To this end, we propose a method based on user-selected samples in the spatial domain to make complex multivariate volume data visualization more accessible for domain users. However, this method still requires users to fine-tune transfer functions in parameter space transfer function widgets, which may not be familiar to them. We therefore propose GuideME, a novel slice-guided semiautomatic multivariate volume exploration approach. GuideME provides the user, an easy-to-use, slice-based user interface that suggests the feature boundaries and allows the user to select features via click and drag, and then an optimal transfer function is automatically generated by optimizing a response function. Throughout the exploration process, the user does not need to interact with the parameter views at all. Finally, real-world multivariate volume datasets are also usually of large size, which is larger than the GPU memory and even the main memory of standard work stations. We propose a ray-guided out-of-core, interactive volume rendering and efficient query method to support large and complex multivariate volumes on standard work stations

Transfer function design based on user selected samples for intuitive multivariate volume exploration

Figure 1: The user interface and the work flow of the system implementing our proposed method. Four closely linked views are shown and labeled, namely: (1) multi-panel view, (2) volume rendering view, (3) projection view and (4) high-dimensional transfer function view. Three stages: (A) data probing, (B) qualitative analysis and (C) optional feature refinement comprise our work flow. With the proposed method and user interface, domain users are able to explore and extract meaningful features in highly complex multivariate dataset, e.g. the 3D seismic survey shown above. Multivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make mul-tivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing fea-tures of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to au-tomatically and robustly generate high dimensional transfer func-tions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimension-ality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets