Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration.

Bingwei Ge, Fatma Najar, Nizar Bouguila

Abstract read

In one paragraph

Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bingwei GeConcordia Institute for Information Systems Engineering, Concordia University, 1515 St. Catherine Street West, Montreal, QC H3G 2W1, Canada.

Fatma NajarConcordia Institute for Information Systems Engineering, Concordia University, 1515 St. Catherine Street West, Montreal, QC H3G 2W1, Canada.

Nizar BouguilaConcordia Institute for Information Systems Engineering, Concordia University, 1515 St. Catherine Street West, Montreal, QC H3G 2W1, Canada.ORCID 0000-0001-7224-7940

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, a weighted multivariate generalized Gaussian mixture model combined with stochastic optimization is proposed for point cloud registration. The mixture model parameters of the target scene and the scene to be registered are updated iteratively by the fixed point method under the framework of the EM algorithm, and the number of components is determined based on the minimum message length criterion (MML). The KL divergence between these two mixture models is utilized as the loss function for stochastic optimization to find the optimal parameters of the transformation model. The self-built point clouds are used to evaluate the performance of the proposed algorithm on rigid registration. Experiments demonstrate that the algorithm dramatically reduces the impact of noise and outliers and effectively extracts the key features of the data-intensive regions.

Indexed as

KL divergenceminimum message lengthmultivariate generalized Gaussianpoint set robust registrationstochastic optimizationweighted-data clustering

Identifiers

PMID37754943

DOI10.3390/jimaging9090179

PMCPMC10532543

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.