Real-time online learning of Gaussian mixture model for opacity mapping

Zhou, Guo; Zhu, Delong; Wei, Yang; Wang, Zhaoqi; Zhou, Yongquan

doi:10.1016/j.neucom.2015.12.135

Cited by 3 publications

(6 citation statements)

References 21 publications

(30 reference statements)

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“…The same was done by Zhou et al [55] , but they focused only on qualitative part due to the not fully optimized proof of concepts. They compared their reconstructed transmittance with some other works such as A-buffer [56] and 8 nodes of [57].…”

Section: Virtual Benchmarksmentioning

confidence: 99%

Photorealistic rendering: a survey on evaluation

Kolivand

Sunar

Kakh³

et al. 2018

Multimed Tools Appl

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Section: Virtual Benchmarksmentioning

confidence: 99%

Photorealistic rendering: a survey on evaluation

Kolivand

Sunar

Kakh³

et al. 2018

Multimed Tools Appl

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“…Under certain conditions, it has been demonstrated experimentally that observations may have no practical contribution to state estimation accuracy after as few as two to five time-steps [15,16]. In such cases, it is computationally more efficient to discard observations that would provide no practical contribution.…”

Section: Introductionmentioning

confidence: 99%

“…Then, one may ask what factors determine whether an observation should be discarded. A variety of experiments have demonstrated that increasing randomness in an object’s motion, decreasing observation noise, and increasing the amount of delay of an observation all decrease its contribution to estimation accuracy [15,16,17]. The experiments of [15] suggest that the impact of an observation decreases exponentially with its delay, but it is still obscure why the decrease would be exponential when the motion and measurement uncertainties increase only linearly or quadratically.…”

Section: Introductionmentioning

confidence: 99%

“…The experiments in [15,16] suggest that we should focus our attention on the ratio of an object’s motion, relative to the observations that are made of it. (This dimensionless quantity is known as the maneuvering index [6,9,18,19]).…”

Section: Introductionmentioning

confidence: 99%

“…Dropping some old observations improves computational efficiency while maintaining nearly-optimal performance [15,16,17]. …”

Section: Introductionmentioning

confidence: 99%

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Exploring the Exponentially Decaying Merit of an Out-of-Sequence Observation

Yoder

Baek

Kwon

et al. 2018

Sensors

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It is well known that in a Kalman filtering framework, all sensor observations or measurements contribute toward improving the accuracy of state estimation, but, as observations become older, their impact toward improving estimations becomes smaller to the point that they offer no practical benefit. In this paper, we provide an practical technique for determining the merit of an old observation using system parameters. We demonstrate that the benefit provided by an old observation decreases exponentially with the number of observations captured and processed after it. To quantify the merit of an old observation, we use the filter gain for the delayed observation, found by re-processing all past measurements between the delayed observation and the current time estimate, a high cost task. We demonstrate the value of the proposed technique to system designers using both nearly-constant position (random walk) and nearly-constant velocity (discrete white-noise acceleration, DWNA) cases. In these cases, the merit (that is, gain) of an old observation can be computed in closed-form without iteration. The analysis technique incorporates the state transition function, the observation function, the state transition noise, and the observation noise to quantify the merit of an old observation. Numerical simulations demonstrate the accuracy of these predictions even when measurements arrive randomly according to a Poisson distribution. Simulations confirm that our approach correctly predicts which observations increase estimation accuracy based on their delay by comparing a single-step out-of-sequence Kalman filter with a selective version that drops out-of-sequence observations. This approach may be used in system design to evaluate feasibility of a multi-agent target tracking system, and when selecting system parameters including sensor rates and network latencies.

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A novel statistical approach for clustering positive data based on finite inverted Beta-Liouville mixture models

Fan

et al. 2019

Neurocomputing

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Nowadays, a great number of positive data has been occurred naturally in many applications, however, it was not adequately analyzed. In this article, we propose a novel statistical approach for clustering multivariate positive data. Our approach is based on a finite mixture model of inverted Beta-Liouville (IBL) distributions, which is proper choice for modeling and analysis of positive vector data. We develop two different approaches to learn the proposed mixture model. Firstly, the maximum likelihood (ML) is utilized to estimate parameters of the finite inverted Beta-Liouville mixture model in which the right number of mixture components is determined according to the minimum message length (MML) criterion. Secondly, the variational Bayes (VB) is adopted to learn our model where the parameters and the number of mixture components can be determined simultaneously in a unified framework, without the requirement of using information criteria. We investigate the effectiveness of our model by conducting a series of experiments on both synthetic and real data sets.

show abstract

Real-time online learning of Gaussian mixture model for opacity mapping

Cited by 3 publications

References 21 publications

Photorealistic rendering: a survey on evaluation

Photorealistic rendering: a survey on evaluation

Exploring the Exponentially Decaying Merit of an Out-of-Sequence Observation

A novel statistical approach for clustering positive data based on finite inverted Beta-Liouville mixture models

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