Probabilistic NeuroScale for Uncertainty Visualisation

Sivaraksa, M.; Lowe, David

doi:10.1109/iv.2009.106

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…We restrict the implementations in this paper to the creation of optimal two-dimensional data representations. Optimality is defined by minimizing a specific distortion measure, the STRESS between the original data space and the projected two-dimensional data representation space, following the approach in [13] and developed in [26]. By construction, this two-dimensional representation will be structure preserving such that neighborhoods and the pairwise relative dissimilarities between observations will be preserved.…”

Section: Data Projectionmentioning

confidence: 99%

“…Further details of the full shadow targets algorithm can be found in [30] and [13] for a probabilistic version. This completes the description of all of the components of the STRESS measure (7), and its optimization to create a principled transformation process, which preserves structural relationships in the original data.…”

Section: Data Projectionmentioning

confidence: 99%

“…To affect the reduction of high data dimensionality of sonar arrays into a visualization space as well as deal with the noisy and uncertain sonar signals, we will utilize a Probabilistic NeuroScale model [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar

Rice

Lowe

2019

IEEE J. Oceanic Eng.

Self Cite

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Abstract-Creating human-informative signal processing systems for the underwater acoustic environment that do not generate operator cognitive saturation and overload is a major challenge. To alleviate cognitive operator overload, we present a visual analytics methodology in which multiple beam-formed sonar returns are mapped to an optimized 2-D visual representation, which preserves the relevant data structure. This representation alerts the operator as to which beams are likely to contain anomalous information by modeling a latent distribution of information for each beam. Sonar operators therefore focus their attention only on the surprising events. In addition to the principled visualization of high-dimensional uncertain data, the system quantifies anomalous information using a Fisher Information measure. Central to this process is the novel use of both signal and noise observation modeling to characterize the sensor information. A demonstration of detecting exceptionally low signal-to-noise ratio targets embedded in real-world 33-beam passive sonar data is presented.

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Section: Data Projectionmentioning

confidence: 99%

Section: Data Projectionmentioning

confidence: 99%

See 1 more Smart Citation

A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar

Rice

Lowe

2019

IEEE J. Oceanic Eng.

Self Cite

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“…Iwata et al 27 warped a latent mixture of Gaussians to visualize high-dimensional data as nonparametric cluster shapes with color maps. Sivaraksa and Lowe 28 used Probabilistic NeuroScale for uncertainty visualization. In their work, data in high-dimensional space are projected to a plane by minimizing a cost function similar to a mass–spring model used in this work.…”

Section: Related Workmentioning

confidence: 99%

Visualizing fuzzy sets using opacity-varying freeform diagrams

Zhu

Xia

Jia

et al. 2017

Information Visualization

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As an extension of classical sets, fuzzy sets allow their containing elements to have continuous membership, which broadens its applications in various domains. In order to visualize the uncertain owner–member relationship, we design an intuitive diagram to visualize fuzzy sets. The diagram has a freeform boundary, which allows a clear layout of the data. In addition, the opacity of the diagram reveals the uncertainty of the membership. Physical simulation and bi-harmonic interpolation are introduced to generate the proposed diagram. We test our method with different fuzzy sets. The results show that our method is efficient to generate intuitive visualization of fuzzy sets.

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“…The standard pointwise NeuroScale architecture was extended in [10] to accomodate a restricted level of uncertainty in a probabilistic framework. The uncertainties in the observed high-dimensional space, namely the noise models in the singlebeam case, or the covariance matrices in the multi-beam model, are preserved in the mapping where only the means of the low dimensional distributions are changed in the mapping phase.…”

Section: B Probabilistic Neuroscalementioning

confidence: 99%

Topographic visual analytics of multibeam dynamic SONAR data

Rice

Lowe

2014

2014 Sensor Signal Processing for Defence (SSPD)

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Abstract-This paper considers the problem of lowdimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point relative similarity measures) appropriate to the below-water maritime domain, we are investigating a preliminary prototype topographic visualisation model. The focus of the current paper is on the mathematical problem of exploiting a relative dissimilarity representation of signals in a visual informatics mapping model, driven by real-world sonar systems. A realistic noise model is explored and incorporated into non-linear and topographic visualisation algorithms building on the approach of [9]. Concepts are illustrated using a real world dataset of 32 hydrophones monitoring a shallow-water environment in which targets are present and dynamic.

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Probabilistic NeuroScale for Uncertainty Visualisation

Cited by 3 publications

References 6 publications

A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar

A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar

Visualizing fuzzy sets using opacity-varying freeform diagrams

Topographic visual analytics of multibeam dynamic SONAR data

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