Arellano, Claudia and Dahyot, Rozenn (2012) Shape Model Fitting Using non-Isotropic GMM. IET Irish Signals and Systems Conference (ISSC 2012).

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Abstract

We present a Mean Shift algorithm for fitting shape models. This algorithm maximises a posterior density function where the likelihood is defined as the Euclidean distance between two Gaussian mixture density functions, one modelling the observations while the other corresponds to the shape model. We explore the role of the covariance matrix in the Gaussian kernel for encoding the shape of the model in the density function. Results show that using non-isotropic covariance matrices improve the efficiency of the algorithm and allow to reduce the number of kernels to use in the mixture without compromising the robustness of the algorithm.

Item Type:	Article
Keywords:	Morphable Models; Fitting Algorithm; Gaussian Mixture Models; Mean Shift;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15273
Identification Number:	https://doi.org/10.1049/ic.2012.0196
Depositing User:	Laura Gallagher
Date Deposited:	18 Jan 2022 16:54
Journal or Publication Title:	IET Irish Signals and Systems Conference (ISSC 2012)
Publisher:	IET
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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