Diffusion-based spatial priors for imaging

We describe a Bayesian scheme to analyze images, which uses spatial priors encoded by a diffusion kernel, based on a weighted graph Laplacian. This provides a general framework to formulate a spatial model, whose parameters can be optimized. The application we have in mind is a spatiotemporal model...

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Detalles Bibliográficos
Autores principales: Harrison, L.M., Penny, W., Ashburner, J., Trujillo-Barreto, N., Friston, K.J.
Formato: Texto
Lenguaje:English
Publicado: Academic Press 2007
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2643839/
https://www.ncbi.nlm.nih.gov/pubmed/17869542
http://dx.doi.org/10.1016/j.neuroimage.2007.07.032

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2643839/
https://www.ncbi.nlm.nih.gov/pubmed/17869542
http://dx.doi.org/10.1016/j.neuroimage.2007.07.032

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