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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: | 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 |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/15273 |
| 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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