Name
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. |
Year | Venue | Keywords |
|---|---|---|
2012 | European Signal Processing Conference | Mean Shift,Gaussian Mixture Models,Morphable Models,Shape Fitting |
Field | DocType | ISSN |
Point distribution model,Active shape model,Gaussian random field,Pattern recognition,Euclidean distance,Algorithm,Gaussian,Gaussian process,Artificial intelligence,Mean-shift,Mixture model,Mathematics | Conference | 2076-1465 |
Citations | PageRank | References |
2 | 0.39 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudia Arellano | 1 | 9 | 1.82 |
| Rozenn Dahyot | 2 | 340 | 32.62 |