6,966 research outputs found
MCMC joint separation and segmentation of hidden Markov fields
In this contribution, we consider the problem of the blind separation of
noisy instantaneously mixed images. The images are modelized by hidden Markov
fields with unknown parameters. Given the observed images, we give a Bayesian
formulation and we propose to solve the resulting data augmentation problem by
implementing a Monte Carlo Markov Chain (MCMC) procedure. We separate the
unknown variables into two categories:
1. The parameters of interest which are the mixing matrix, the noise
covariance and the parameters of the sources distributions. 2. The hidden
variables which are the unobserved sources and the unobserved pixels
classification labels.
The proposed algorithm provides in the stationary regime samples drawn from
the posterior distributions of all the variables involved in the problem
leading to a flexibility in the cost function choice.
We discuss and characterize some problems of non identifiability and
degeneracies of the parameters likelihood and the behavior of the MCMC
algorithm in this case.
Finally, we show the results for both synthetic and real data to illustrate
the feasibility of the proposed solution. keywords: MCMC, blind source
separation, hidden Markov fields, segmentation, Bayesian approachComment: Presented at NNSP2002, IEEE workshop Neural Networks for Signal
Processing XII, Sept. 2002, pp. 485--49
Exploring the structure of a real-time, arbitrary neural artistic stylization network
In this paper, we present a method which combines the flexibility of the
neural algorithm of artistic style with the speed of fast style transfer
networks to allow real-time stylization using any content/style image pair. We
build upon recent work leveraging conditional instance normalization for
multi-style transfer networks by learning to predict the conditional instance
normalization parameters directly from a style image. The model is successfully
trained on a corpus of roughly 80,000 paintings and is able to generalize to
paintings previously unobserved. We demonstrate that the learned embedding
space is smooth and contains a rich structure and organizes semantic
information associated with paintings in an entirely unsupervised manner.Comment: Accepted as an oral presentation at British Machine Vision Conference
(BMVC) 201
A stochastic algorithm for probabilistic independent component analysis
The decomposition of a sample of images on a relevant subspace is a recurrent
problem in many different fields from Computer Vision to medical image
analysis. We propose in this paper a new learning principle and implementation
of the generative decomposition model generally known as noisy ICA (for
independent component analysis) based on the SAEM algorithm, which is a
versatile stochastic approximation of the standard EM algorithm. We demonstrate
the applicability of the method on a large range of decomposition models and
illustrate the developments with experimental results on various data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS499 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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