Shape model fitting algorithm without point correspondence

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...

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Main Authors: Arellano, C., Dahyot, R.
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Data models
Euclidean distance
Gaussian Mixture Models
Mean Shift
Morphable Models
Robustness
Shape
Shape Fitting
Signal processing algorithms
Solid modeling
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creator Arellano, C.
Dahyot, R.
description 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.
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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. 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2219-5491
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source IEEE Xplore All Conference Series
subjects Computational modeling
Data models
Euclidean distance
Gaussian Mixture Models
Mean Shift
Morphable Models
Robustness
Shape
Shape Fitting
Signal processing algorithms
Solid modeling
title Shape model fitting algorithm without point correspondence
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