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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Bibliographic Details
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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Summary: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.
ISSN:2219-5491
2219-5491