BookDOI
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods
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The article was published on 2003-01-01. It has received 301 citations till now. The article focuses on the topics: Markov chain Monte Carlo & Hybrid Monte Carlo.read more
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Journal ArticleDOI
An introduction to factor graphs
TL;DR: This work uses Forney-style factor graphs, which support hierarchical modeling and are compatible with standard block diagrams, and uses them to derive practical detection/estimation algorithms in a wide area of applications.
Journal ArticleDOI
A tutorial on adaptive MCMC
TL;DR: This work proposes a series of novel adaptive algorithms which prove to be robust and reliable in practice and reviews criteria and the useful framework of stochastic approximation, which allows one to systematically optimise generally used criteria.
Journal ArticleDOI
An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis
Paul Schmidt,Christian Gaser,Milan Arsic,Dorothea Buck,Annette Förschler,Achim Berthele,Muna Hoshi,Rüdiger Ilg,Volker Schmid,Claus Zimmer,Bernhard Hemmer,Mark Mühlau +11 more
TL;DR: The developed and evaluated an algorithm for automated lesion detection requiring a three-dimensional (3D) gradient echo (GRE) T1-weighted and a FLAIR image at 3 Tesla and it is believed that this tool allows fast and reliable segmentation of FLAIR-hyperintense lesions, which might simplify the quantification of lesions in basic research and even clinical trials.
Journal ArticleDOI
Universal distortion function for steganography in an arbitrary domain
TL;DR: This paper proposes a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be applied for embedding in an arbitrary domain and demonstrates experimentally using rich models as well as targeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art in the spatial domain, JPEG domain, and side-informed JPEG domain.
Proceedings Article
On Contrastive Divergence Learning.
TL;DR: The properties of CD learning are studied and it is shown that it provides biased estimates in general, but that the bias is typically very small.
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