VOIDGA: A View-Approximation Oriented Image Database Generation Approach

Lukasczyk, Jonas; Kinner, Eric Georg; Ahrens, James; Leitte, Heike; Garth, Christoph

doi:10.1109/ldav.2018.8739204

Cited by 8 publications

(11 citation statements)

References 37 publications

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“…To facilitate practically usability, it appears possible to introduce automation of parameters (such as number of persistence intervals or camera angles stored) through heuristics or optimization techniques, to keep the generated database within a given budget while maximizing post hoc flexibility, or alternatively, to ensure a specified degree of flexibility while minimizing database size. For example, the integration of the VOIDGA approach [18] could reduce the number of stored camera locations, which would allow to sample other parameters more densely. Finally, database generation could benefit from low-level technical improvements, such as different compression methods and data formats.…”

Section: Discussionmentioning

confidence: 99%

“…Originally, these databases are structured image collections that enable the interactive post hoc visual analysis of extreme-scale simulations by simply browsing images that have been stored in situ for a fixed sampling of the parameter space. The current specification [25] supports any kind of data product; in particular depth images that can be used to composite 3D renderings of the scene post hoc [18]. Biedert et al [4] also investigated the Cinema-inspired idea of combining in situ topological analysis and simplification with compact image-based storage in socalled contour tree depth images, which record at each pixel the list of depth values of individual contours from front to back.…”

Section: Related Workmentioning

confidence: 99%

“…Database viewers then enable users to browse the structured image stores by selecting interesting parameter combinations from parallel coordinate plots [35], by performing queries [3,32], or by snapping to the closest available camera locations while navigating an emulated 3D view [21]. It is also possible to approximate the depicted surfaces to enable free camera movement [18]. However, a limitation of Cinema databases is that the flexibility of the post hoc analysis is limited by the generated images.…”

Section: Image Generationmentioning

confidence: 99%

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Dynamic Nested Tracking Graphs

Lukasczyk

Garth

Weber

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. The topology-based visual analytics framework supports the feature-centered navigation of Cinema databases consisting of image and analysis products generated during large-scale simulation runs, where numerous features are organized into a manageable amount of hierarchical groups that can be explored in a level-of-detail approach. Here, the interface shows an ensemble member of the viscous finger dataset, where colors encode individual fingers for salt concentration level 30. The prime interaction device of the framework is a nested tracking graph (NTG) that simultaneously displays the temporal evolution of superlevel set components for multiple levels (bottom). The NTG is used to navigate through time and retrieve component images from the database (top left), whereas the split tree (top center) and persistence diagram (top right) support the user in selecting important levels and filter criteria.Abstract-This work describes an approach for the interactive visual analysis of large-scale simulations, where numerous superlevel set components and their evolution are of primary interest. The approach first derives, at simulation runtime, a specialized Cinema database that consists of images of component groups, and topological abstractions. This database is processed by a novel graph operation-based nested tracking graph algorithm (GO-NTG) that dynamically computes NTGs for component groups based on size, overlap, persistence, and level thresholds. The resulting NTGs are in turn used in a feature-centered visual analytics framework to query specific database elements and update feature parameters, facilitating flexible post hoc analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Image Generationmentioning

confidence: 99%

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Dynamic Nested Tracking Graphs

Lukasczyk

Garth

Weber

et al. 2020

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…A Cinema database [2] is a large collection of images which are sampled based on time, visualization object and camera position, and stored along with metadata that allows interactive querying [25]. Cinema is used with image processing techniques to combine images to obtain new camera and time locations or even to reconstruct the original object using Depth Image Based Rendering [19]. Cinema has been implemented in ParaView as well as the open source Topology Toolkit TTK [30].…”

Section: The Cinema In Situ Databasementioning

confidence: 99%

“…After single contour extraction in situ, the first stage is complete, and we save depth images from varying camera positions for later reconstruction [19] based on a TTK [30] implementation in order to avoid saving large meshes of millions of triangles.…”

Section: Cinema Integrationmentioning

confidence: 99%

Data Parallel Hypersweeps for in Situ Topological Analysis

Hristov

Weber

Carr

et al. 2020

2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV)

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The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze large data sets in situ while the simulation is running. Unfortunately, methods for using the contour tree for practical data analysis are still primarily serial, including single isocontour extraction, branch decomposition and simplification. We report data parallel methods for these tasks using a data structure called the hyperstructure and a general purpose approach called a hypersweep. We implement and integrate these methods with a Cinema database that stores features as depth images and with a web server that reconstructs the features for direct visualization.

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An Overview of the Topology ToolKit

Masood

Budin

Falk

et al. 2021

Mathematics and Visualization

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This software paper gives an overview of the features supported by the Topology ToolKit (TTK), which is an open-source library for topological data analysis (TDA). TTK implements, in a generic and efficient way, a substantial collection of reference algorithms in TDA. Since its initial public release in 2017, both its user and developer bases have grown, resulting in a significant increase in the number of supported features. In contrast to the original paper introducing TTK [40] (which detailed the core algorithms and data structures of TTK), the purpose of this software paper is to describe the list of features currently supported by TTK, ranging from image segmentation tools to advanced topological analysis of high-dimensional data, with concrete usage examples available on the TTK website [42].

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VOIDGA: A View-Approximation Oriented Image Database Generation Approach

Cited by 8 publications

References 37 publications

Dynamic Nested Tracking Graphs

Dynamic Nested Tracking Graphs

Data Parallel Hypersweeps for in Situ Topological Analysis

An Overview of the Topology ToolKit

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