107 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
Food Safety Self Inspection Form
Food preparation issues such as records, reheating, cooking temperatures, cooling, holding times and temperatures, separation and segmentation, personnel and personal contact with foods are examined
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.Comment: Accepted by Journal of Structural Biolog
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