Distribution-driven visualization of volume data

An Intelligent System Approach for Probabilistic Volume Rendering Using Hierarchical 3D Convolutional Sparse Coding

In this paper, we propose a novel machine learning-based voxel classification method for highly-accurate volume rendering. Unlike conventional voxel classification methods that incorporate intensity-based features, the proposed method employs dictionary based features learned directly from the input data using hierarchical multi-scale 3D convolutional sparse coding, a novel extension of the state-of-the-art learning-based sparse feature representation method. The proposed approach automatically generates high-dimensional feature vectors in up to 75 dimensions, which are then fed into an intelligent system built on a random forest classifier for accurately classifying voxels from only a handful of selection scribbles made directly on the input data by the user. We apply the probabilistic transfer function to further customize and refine the rendered result. The proposed method is more intuitive to use and more robust to noise in comparison with conventional intensity-based classification methods. We evaluate the proposed method using several synthetic and real-world volume datasets, and demonstrate the methods usability through a user study.

In Situ Distribution Guided Analysis and Visualization of Transonic Jet Engine Simulations

Study of flow instability in turbine engine compressors is crucial to understand the inception and evolution of engine stall. Aerodynamics experts have been working on detecting the early signs of stall in order to devise novel stall suppression technologies. A state-of-the-art Navier-Stokes based, time-accurate computational fluid dynamics simulator, TURBO, has been developed in NASA to enhance the understanding of flow phenomena undergoing rotating stall. Despite the proven high modeling accuracy of TURBO, the excessive simulation data prohibits post-hoc analysis in both storage and I/O time. To address these issues and allow the expert to perform scalable stall analysis, we have designed an in situ distribution guided stall analysis technique. Our method summarizes statistics of important properties of the simulation data in situ using a probabilistic data modeling scheme. This data summarization enables statistical anomaly detection for flow instability in post analysis, which reveals the spatiotemporal trends of rotating stall for the expert to conceive new hypotheses. Furthermore, the verification of the hypotheses and exploratory visualization using the summarized data are realized using probabilistic visualization techniques such as uncertain isocontouring. Positive feedback from the domain scientist has indicated the efficacy of our system in exploratory stall analysis.

Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis

Effective analysis of features in time-varying data is essential in numerous scientific applications. Feature extraction and tracking are two important tasks scientists rely upon to get insights about the dynamic nature of the large scale time-varying data. However, often the complexity of the scientific phenomena only allows scientists to vaguely define their feature of interest. Furthermore, such features can have varying motion patterns and dynamic evolution over time. As a result, automatic extraction and tracking of features becomes a non-trivial task. In this work, we investigate these issues and propose a distribution driven approach which allows us to construct novel algorithms for reliable feature extraction and tracking with high confidence in the absence of accurate feature definition. We exploit two key properties of an object, motion and similarity to the target feature, and fuse the information gained from them to generate a robust feature-aware classification field at every time step. Tracking of features is done using such classified fields which enhances the accuracy and robustness of the proposed algorithm. The efficacy of our method is demonstrated by successfully applying it on several scientific data sets containing a wide range of dynamic time-varying features.

ViSlang: A System for Interpreted Domain-Specific Languages for Scientific Visualization

Researchers from many domains use scientific visualization in their daily practice. Existing implementations of algorithms usually come with a graphical user interface (high-level interface), or as software library or source code (low-level interface). In this paper we present a system that integrates domain-specific languages (DSLs) and facilitates the creation of new DSLs. DSLs provide an effective interface for domain scientists avoiding the difficulties involved with low-level interfaces and at the same time offering more flexibility than high-level interfaces. We describe the design and implementation of ViSlang, an interpreted language specifically tailored for scientific visualization. A major contribution of our design is the extensibility of the ViSlang language. Novel DSLs that are tailored to the problems of the domain can be created and integrated into ViSlang. We show that our approach can be added to existing user interfaces to increase the flexibility for expert users on demand, but at the same time does not interfere with the user experience of novice users. To demonstrate the flexibility of our approach we present new DSLs for volume processing, querying and visualization. We report the implementation effort for new DSLs and compare our approach with Matlab and Python implementations in terms of run-time performance.

A survey of visualization pipelines

The most common abstraction used by visualization libraries and applications today is what is known as the visualization pipeline. The visualization pipeline provides a mechanism to encapsulate algorithms and then couple them together in a variety of ways. The visualization pipeline has been in existence for over 20 years, and over this time many variations and improvements have been proposed. This paper provides a literature review of the most prevalent features of visualization pipelines and some of the most recent research directions.

Modified Dendrogram of Attribute Space for Multidimensional Transfer Function Design

We introduce a modified dendrogram (MD) (with subtrees to represent clusters) and display it in 2D for multidimensional transfer function design. Such a transfer function for direct volume rendering employs a multidimensional space, termed attribute space. The MD reveals the hierarchical structure information of the attribute space. The user can design a transfer function in an intuitive and informative manner using the MD user interface in 2D instead of multidimensional space, where it is hard to ascertain the relationship of the space. In addition, we provide the capability to interactively modify the granularity of the MD. The coarse-grained MD primarily shows the global information of the attribute space while the fine-grained MD reveals the finer details, and the separation ability of the attribute space is completely preserved in the finest granularity. With this so called multigrained method, the user can efficiently create a transfer function using the coarse-grained MD, and then fine tune it with the fine-grained MDs. Our method is independent on the type of the attributes and supports arbitrary-dimension attribute space.

Skeleton Cuts--An Efficient Segmentation Method for Volume Rendering

Volume rendering has long been used as a key technique for volume data visualization, which works by using a transfer function to map color and opacity to each voxel. Many volume rendering approaches proposed so far for voxels classification have been limited in a single global transfer function, which is in general unable to properly visualize interested structures. In this paper, we propose a localized volume data visualization approach which regards volume visualization as a combination of two mutually related processes: the segmentation of interested structures and the visualization using a locally designed transfer function for each individual structure of interest. As shown in our work, a new interactive segmentation algorithm is advanced via skeletons to properly categorize interested structures. In addition, a localized transfer function is subsequently presented to assign optical parameters via interested information such as intensity, thickness and distance. As can be seen from the experimental results, the proposed techniques allow to appropriately visualize interested structures in highly complex volume medical data sets.