Moment Invariants for 2D Flow Fields Using Normalization

Bujack, Roxana; Hotz, Ingrid; Scheuermann, Gerik; Hitzer, Eckhard

doi:10.1109/pacificvis.2014.16

Cited by 22 publications

(16 citation statements)

References 19 publications

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“…This requires to sample all possible rotations, translations and scales, and typically leads to high running times. Moment-invariant descriptors are used by Schlemmer et al [28] and Bujack et al [3] to achieve pattern invariance with respect to translation, rotation, and scaling. These approaches are fast, but treat 2D vector fields only.…”

Section: Related Workmentioning

confidence: 99%

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Multi-field Pattern Matching based on Sparse Feature Sampling

Wang

Seidel

Weinkauf

2016

IEEE Trans. Visual. Comput. Graphics

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We present an approach to pattern matching in 3D multi-field scalar data. Existing pattern matching algorithms work on single scalar or vector fields only, yet many numerical simulations output multi-field data where only a joint analysis of multiple fields describes the underlying phenomenon fully. Our method takes this into account by bundling information from multiple fields into the description of a pattern. First, we extract a sparse set of features for each 3D scalar field using the 3D SIFT algorithm (Scale-Invariant Feature Transform). This allows for a memory-saving description of prominent features in the data with invariance to translation, rotation, and scaling. Second, the user defines a pattern as a set of SIFT features in multiple fields by e.g. brushing a region of interest. Third, we locate and rank matching patterns in the entire data set. Experiments show that our algorithm is efficient in terms of required memory and computational efforts.

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Section: Related Workmentioning

confidence: 99%

“…Pattern matching algorithms have proven useful for scalar [10,12,22] and vector fields [3,9,13,33]. The general idea is to compute the similarity between a user-supplied pattern and every location in the data set.…”

Section: Introductionmentioning

confidence: 99%

Multi-field Pattern Matching based on Sparse Feature Sampling

Wang

Seidel

Weinkauf

2016

IEEE Trans. Visual. Comput. Graphics

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“…For 2D flows, Schlemmer et al [7] and Bujack et al [8] both leveraged moment invariants to detect 2D flow features. Wei et al [9] relied on user-sketched 2D curves to retrieve similar occurrences from a 3D flow field.…”

Section: Related Workmentioning

confidence: 99%

Extracting flow features via supervised streamline segmentation

Wang

Shene

2015

Computers & Graphics

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“…Now more visualization methods are developed to detect, identify and extract interesting features from the flow field. One genre is to manually define features at first, after which the system will try to detect corresponding features from flow field [28,46,39,8]. Salzbrunn et al [39] has shown that any suitable set of pathline predicates can be interpreted as features in unsteady flow structures.…”

Section: Introductionmentioning

confidence: 99%

FLDA: Latent Dirichlet Allocation Based Unsteady Flow Analysis

Fan

Lai

Guo

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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In this paper, we present a novel feature extraction approach called FLDA for unsteady flow fields based on Latent Dirichlet allocation (LDA) model. Analogous to topic modeling in text analysis, in our approach, pathlines and features in a given flow field are defined as documents and words respectively. Flow topics are then extracted based on Latent Dirichlet allocation. Different from other feature extraction methods, our approach clusters pathlines with probabilistic assignment, and aggregates features to meaningful topics at the same time. We build a prototype system to support exploration of unsteady flow field with our proposed LDA-based method. Interactive techniques are also developed to explore the extracted topics and to gain insight from the data. We conduct case studies to demonstrate the effectiveness of our proposed approach.

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Moment Invariants for 2D Flow Fields Using Normalization

Cited by 22 publications

References 19 publications

Multi-field Pattern Matching based on Sparse Feature Sampling

Multi-field Pattern Matching based on Sparse Feature Sampling

Extracting flow features via supervised streamline segmentation

FLDA: Latent Dirichlet Allocation Based Unsteady Flow Analysis

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