@inproceedings{475fb1e44168405982f47a12b0f51221,
title = "Shape model fitting algorithm without point correspondence",
abstract = "In this paper, we present a Mean Shift algorithm that does not require point correspondence to fit shape models. The observed data and the shape model are represented as mixtures of Gaussians. Using a Bayesian framework, we propose to model the likelihood using the Euclidean distance between the two Gaussian mixture density functions while the latent variables are modelled with a Gaussian prior. We show the performance of our MS algorithm for fitting a 2D hand model and a 3D Morphable Model of faces to point clouds.",
keywords = "Gaussian Mixture Models, Mean Shift, Morphable Models, Shape Fitting",
author = "Claudia Arellano and Rozenn Dahyot",
year = "2012",
language = "English",
isbn = "9781467310680",
series = "European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
pages = "934--938",
booktitle = "Proceedings of the 20th European Signal Processing Conference, EUSIPCO 2012",
note = "20th European Signal Processing Conference, EUSIPCO 2012 ; Conference date: 27-08-2012 Through 31-08-2012",
}