DOAJ Open Access 2023

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

Bingwei Ge Fatma Najar Nizar Bouguila

Abstrak

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.

Topik & Kata Kunci

Photography Computer applications to medicine. Medical informatics Electronic computers. Computer science

Penulis (3)

Bingwei Ge

Fatma Najar

Nizar Bouguila

Format Sitasi

APA MLA BibTeX

Ge, B., Najar, F., Bouguila, N. (2023). Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration. https://doi.org/10.3390/jimaging9090179

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.3390/jimaging9090179
Akses: Open Access ✓