Probabilistic Registration for Gaussian Process Three-Dimensional Shape Modelling in the Presence of Extensive Missing Data
Articolo
Data di Pubblicazione:
2023
Citazione:
Probabilistic Registration for Gaussian Process Three-Dimensional Shape Modelling in the Presence of Extensive Missing Data / F. Marreiros, R. Ferreira, A. Micheletti, C. Soares. - In: SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE. - ISSN 2577-0187. - 5:2(2023), pp. 502-527. [10.1137/22M1495494]
Abstract:
We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data. Gaussian Processes are a proven powerful tool, as they provide a unified setting for shape modelling and fitting. While the existing methods on this area prove to work well for the general case of the human head, when looking at more detailed and deformed data, with a high prevalence of missing data, such as the ears, the results are not satisfactory. In order to overcome this, we formulate the shape fitting problem as a multi-annotator Gaussian Process Regression and establish a parallel with the standard probabilistic registration. The achieved method SFGP shows better performance when dealing with extensive areas of missing data when compared to a state-of-the-art registration method and current approaches for registration with GP. Experiments are conducted both for a 2D small dataset with several transformations and a 3D dataset of ears
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Gaussian Processes; Shape modelling; Registration; Variational Bayes;
Elenco autori:
F. Marreiros, R. Ferreira, A. Micheletti, C. Soares
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