Four-dimensional dynamic flow measurement by holographic particle image velocimetry

The ultimate goal of holographic particle image velocimetry (HPIV) is to provide space- and time-resolved measurement of complex flows. Recent new understanding of holographic imaging of small particles, pertaining to intrinsic aberration and noise in particular, has enabled us to elucidate fundamental issues in HPIV and implement a new HPIV system. This system is based on our previously reported off-axis HPIV setup, but the design is optimized by incorporating our new insights of holographic particle imaging characteristics. Furthermore, the new system benefits from advanced data processing algorithms and distributed parallel computing technology. Because of its robustness and efficiency, for the first time to our knowledge, the goal of both temporally and spatially resolved flow measurements becomes tangible. We demonstrate its temporal measurement capability by a series of phase-locked dynamic measurements of instantaneous three-dimensional, three-component velocity fields in a highly three-dimensional vortical flow--the flow past a tab

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.Comment: Accepted by Journal of Structural Biolog

Fine-grained visualization pipelines and lazy functional languages

The pipeline model in visualization has evolved from a conceptual model of data processing into a widely used architecture for implementing visualization systems. In the process, a number of capabilities have been introduced, including streaming of data in chunks, distributed pipelines, and demand-driven processing. Visualization systems have invariably built on stateful programming technologies, and these capabilities have had to be implemented explicitly within the lower layers of a complex hierarchy of services. The good news for developers is that applications built on top of this hierarchy can access these capabilities without concern for how they are implemented. The bad news is that by freezing capabilities into low-level services expressive power and flexibility is lost. In this paper we express visualization systems in a programming language that more naturally supports this kind of processing model. Lazy functional languages support fine-grained demand-driven processing, a natural form of streaming, and pipeline-like function composition for assembling applications. The technology thus appears well suited to visualization applications. Using surface extraction algorithms as illustrative examples, and the lazy functional language Haskell, we argue the benefits of clear and concise expression combined with fine-grained, demand-driven computation. Just as visualization provides insight into data, functional abstraction provides new insight into visualization

Visualization, Exploration and Data Analysis of Complex Astrophysical Data

In this paper we show how advanced visualization tools can help the researcher in investigating and extracting information from data. The focus is on VisIVO, a novel open source graphics application, which blends high performance multidimensional visualization techniques and up-to-date technologies to cooperate with other applications and to access remote, distributed data archives. VisIVO supports the standards defined by the International Virtual Observatory Alliance in order to make it interoperable with VO data repositories. The paper describes the basic technical details and features of the software and it dedicates a large section to show how VisIVO can be used in several scientific cases.Comment: 32 pages, 15 figures, accepted by PAS

Interactive Isocontouring of High-Order Surfaces

Scientists and engineers are making increasingly use of hp-adaptive discretization methods to compute simulations. While techniques for isocontouring the high-order data generated by these methods have started to appear, they typically do not facilitate interactive data exploration. This work presents a novel interactive approach for approximate isocontouring of high-order data. The method is based on a two-phase hybrid rendering algorithm. In the first phase, coarsely seeded particles are guided by the gradient of the field for obtaining an initial sampling of the isosurface in object space. The second phase performs ray casting in the image space neighborhood of the initial samples. Since the neighborhood is small, the initial guesses tend to be close to the isosurface, leading to accelerated root finding and thus efficient rendering. The object space phase affects the density of the coarse samples on the isosurface, which can lead to holes in the final rendering and overdraw. Thus, we also propose a heuristic, based on dynamical systems theory, that adapts the neighborhood of the seeds in order to obtain a better coverage of the surface. Results for datasets from computational fluid dynamics are shown and performance measurements for our GPU implementation are given

Variational Level-Set Detection of Local Isosurfaces from Unstructured Point-based Volume Data

A standard approach for visualizing scalar volume data is the extraction of isosurfaces. The most efficient methods for surface extraction operate on regular grids. When data is given on unstructured point-based samples, regularization can be applied but may introduce interpolation errors. We propose a method for smooth isosurface visualization that operates directly on unstructured point-based volume data avoiding any resampling. We derive a variational formulation for smooth local isosurface extraction using an implicit surface representation in form of a level-set approach, deploying Moving Least Squares (MLS) approximation, and operating on a kd-tree. The locality of our approach has two aspects: first, our algorithm extracts only those components of the isosurface, which intersect a subdomain of interest; second, the action of the main term in the governing equation is concentrated near the current isosurface position. Both aspects reduce the computation times per level-set iteration. As for most level-set methods a reinitialization procedure is needed, but we also consider a modified algorithm where this step is eliminated. The final isosurface is extracted in form of a point cloud representation. We present a novel point completion scheme that allows us to handle highly adaptive point sample distributions. Subsequently, splat-based or mere (shaded) point rendering is applied. We apply our method to several synthetic and real-world data sets to demonstrate its validity and efficiency

Direct Multifield Volume Ray Casting of Fiber Surfaces

Multifield data are common in visualization. However, reducing these data to comprehensible geometry is a challenging problem. Fiber surfaces, an analogy of isosurfaces to bivariate volume data, are a promising new mechanism for understanding multifield volumes. In this work, we explore direct ray casting of fiber surfaces from volume data without any explicit geometry extraction. We sample directly along rays in domain space, and perform geometric tests in range space where fibers are defined, using a signed distance field derived from the control polygons. Our method requires little preprocess, and enables real-time exploration of data, dynamic modification and pixel-exact rendering of fiber surfaces, and support for higher-order interpolation in domain space. We demonstrate this approach on several bivariate datasets, including analysis of multi-field combustion data