Résumé
Image registration aims to estimate the global deformation between a target image I1 and a reference image I2. In this context, we will focus on estimating a random field U on the I1 domain ω = [0, 1]2 based on observations of U on a finite set of curves β ϵ ω. Indeed, we present a new multimodal image registration method based on Gaussian random fields. The proposed method first find the optimal correspondences between curves βs then estimate the deformation vector field on ω. The optimal solution is computed using Maximum Likelihood and Bayesian inference. Based on results using both real and simulated data, the resulting deformation has the advantage of being exact on the observations as being sufficiently smooth over the whole ω.
| Titre traduit de la contribution | Bayesian inference for estimation of large deformations by Gaussian random fields: Application to multimodal image registration |
|---|---|
| langue originale | Français |
| Pages (de - à) | 43-49 |
| Nombre de pages | 7 |
| journal | CEUR Workshop Proceedings |
| Volume | 2133 |
| état | Publié - 1 janv. 2018 |
| Modification externe | Oui |
| Evénement | 2018 Actes de la Conference Nationale d'Intelligence Artificielle et Rencontres des Jeunes Chercheurs en Intelligence Artificielle, CNIA+RJCIA 2018 - 2018 National Conference on Artificial Intelligence and Meetings of Young Researchers on Artificial Intelligence, CNIA + RJCIA 2018 - Nancy, France Durée: 4 juil. 2018 → 6 juil. 2018 |
mots-clés
- Bayesian inference
- Gaussian process
- Image registration
- Spatial statistics
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