An Inference Language for Imaging

Stefano Pedemonte, Ciprian Catana, Koen Van Leemput

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framework is composed of a set of language primitives and of an inference engine based on a message-passing system that integrates cutting-edge computational tools, including proximal algorithms and high performance Hamiltonian Markov Chain Monte Carlo techniques. A set of domain-specific highly optimized GPU-accelerated primitives specializes iLang to the spatial data-structures that arise in imaging applications. We illustrate the framework through a challenging application: spatio-temporal tomographic reconstruction with compressive sensing.
Original languageEnglish
Title of host publicationRevised Selected Papers of the 1st International Workshop on Bayesian and grAphical Models for Biomedical Imaging (BAMBI 2014)
PublisherSpringer
Publication date2014
Pages61-72
ISBN (Print)978-3-319-12288-5
ISBN (Electronic) 978-3-319-12289-2
DOIs
Publication statusPublished - 2014
Event1st International Workshop on Bayesian and Graphical Models for Biomedical Imaging, BAMBI 2014 - Cambridge, United States
Duration: 18 Sep 2014 → …
Conference number: 1
http://bambi.cs.ucl.ac.uk/

Workshop

Workshop1st International Workshop on Bayesian and Graphical Models for Biomedical Imaging, BAMBI 2014
Number1
CountryUnited States
CityCambridge
Period18/09/2014 → …
OtherIn correlation with the 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Internet address
SeriesLecture Notes in Computer Science
Volume8677
ISSN0302-9743

Cite this

Pedemonte, S., Catana, C., & Van Leemput, K. (2014). An Inference Language for Imaging. In Revised Selected Papers of the 1st International Workshop on Bayesian and grAphical Models for Biomedical Imaging (BAMBI 2014) (pp. 61-72). Springer. Lecture Notes in Computer Science, Vol.. 8677 https://doi.org/10.1007/978-3-319-12289-2_6