Ray-Based Exploration of Large Time-Varying Volume Data Using Per-Ray Proxy Distributions

Wang, Ko-Chih; Wei, Tzu-Hsuan; Shareef, Naeem; Shen, Han‐Wei

doi:10.1109/tvcg.2019.2920130

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…Such a comparison technique could lead to the identification of many false features and therefore is too simple a method for our needs; more complex feature descriptors are generally required. In addition, the inclusion of more complex image quality metrics such as the structural similarity index measure (SSIM) (Wang et al 2004) or peak signal-to-noise ratio (PSNR) would result in a more robust framework. A final image analysis framework, PorosityAnalyzer (Weissenböck et al 2016), compares results from various algorithms, e.g., different blob detection algorithms, to determine which is most useful for a particular application.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, techniques such as Cinema (Ahrens et al 2015;Kageyama and Yamada 2014) project simulation data to a viewing plane, effectively compressing the simulation information via a high-resolution image. Wang et al (2018Wang et al ( , 2019 introduced image proxies combined with statistical distributions to support high-accuracy re-rendering, apply transfer functions and extract iso-surfaces from volumetric data. The effectiveness of each one of these methods to address concerns with large data analysis are discussed in the associated papers.…”

Section: Introductionmentioning

confidence: 99%

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An Image-Based Framework for Ocean Feature Detection and Analysis

et al. 2021

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Today's supercomputing capabilities allow ocean scientists to generate simulation data at increasingly higher spatial and temporal resolutions. However, I/O bandwidth and data storage costs limit the amount of data saved to disk. In situ methods are one solution to generate reduced data extracts, with the potential to reduce disk storage requirement even for high spatial and temporal resolutions, a major advantage to storing raw output. Image proxies have become an efficient and accepted in situ reduced data extract. These extracts require innovative automated techniques to identify and analyze features. We present a framework of computer vision and image processing techniques to detect and analyze important features from in situ image proxies of large ocean simulations. We constrain the analysis framework in support of techniques that emulate ocean-specific tasks as accurately as possible. The framework maximizes feature analysis capabilities while minimizing computational requirements. We demonstrate its use for image proxies extracted from the ocean component of Model for Prediction Across Scales (MPAS) simulations to analyze ocean-specific features such as eddies and western boundary currents. The results obtained for specific data sets are compared to those of traditional methods, documenting the efficacy and advantages of our framework.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Image-Based Framework for Ocean Feature Detection and Analysis

et al. 2021

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“…Dutta et al [1,2,18] used GMM to represent datasets compactly in an in situ environment. Furthermore, Wang et al [4,15,19] used distributions to compactly store volume, time-varying, ensemble datasets for the post-hoc data analysis and visualization. Chaudhur et al [20,21] and Lee et al [12] employed distribution to conduct efficient data query and visualization.…”

Section: Related Work 21 Distribution-based Large Data Processing and Analysismentioning

confidence: 99%

“…The histogram [32,33] is a popular non-parametric distribution representation which has been widely used in many fields [3,15,[34][35][36]. A histogram divides the data value domain into bins and calculates the number (frequency) of input samples that belong to each bin.…”

Section: Histogram Modeling Using Data-parallel Primitivesmentioning

confidence: 99%

“…Our data-parallel primitive-based algorithms can facilitate the data process that is run on supercomputers with different types of computing nodes. In this work, we propose parallel multi-set distribution modeling algorithms for multi-variant histogram [3,[12][13][14][15] and GMM [1,2,4,[15][16][17] modeling, because these are the most popular non-parametric and parametric distribution representations in the scientific data modeling, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Efficient and Portable Distribution Modeling for Large-Scale Scientific Data Processing with Data-Parallel Primitives

2021

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The use of distribution-based data representation to handle large-scale scientific datasets is a promising approach. Distribution-based approaches often transform a scientific dataset into many distributions, each of which is calculated from a small number of samples. Most of the proposed parallel algorithms focus on modeling single distributions from many input samples efficiently, but these may not fit the large-scale scientific data processing scenario because they cannot utilize computing resources effectively. Histograms and the Gaussian Mixture Model (GMM) are the most popular distribution representations used to model scientific datasets. Therefore, we propose the use of multi-set histogram and GMM modeling algorithms for the scenario of large-scale scientific data processing. Our algorithms are developed by data-parallel primitives to achieve portability across different hardware architectures. We evaluate the performance of the proposed algorithms in detail and demonstrate use cases for scientific data processing.

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