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See detailA spatially constrained generative model and an EM algorithm for image segmentation
Diplaros, Aristeidis; Vlassis, Nikos UL; Gevers, Theo

in IEEE Transactions on Neural Networks (2007), 18(3), 798-808

In this paper, we present a novel spatially constrained generative model and an expectation-maximization (EM) algorithm for model-based image segmentation. The generative model assumes that the unobserved ... [more ▼]

In this paper, we present a novel spatially constrained generative model and an expectation-maximization (EM) algorithm for model-based image segmentation. The generative model assumes that the unobserved class labels of neighboring pixels in the image are generated by prior distributions with similar parameters, where similarity is defined by entropic quantities relating to the neighboring priors. In order to estimate model pa rameters from observations, we derive a spatially constrained EM algorithm that iteratively maximizes a lower bound on the data log-likelihood, where the penalty term is data-dependent. Our algorithm is very easy to implement and is similar to the standard EM algorithm for Gaussian mixtures with the main difference that the labels posteriors are "smoothed" over pixels between each E-and M-step by a standard image filter. Experiments on synthetic and real images show that our algorithm achieves competitive segmentation results compared to other Markov-based methods, and is in general faster. [less ▲]

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See detailEfficient source adaptivity in independent component analysis
Vlassis, Nikos UL; Motomura, Y.

in IEEE Transactions on Neural Networks (2001), 12(3), 559-566

A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large ... [more ▼]

A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve "source adaptivity" by directly estimating from the data the "'true" score functions of the sources. In this paper we describe an efficient scheme for achieving this by extending the fast density estimation method of Silverman (1982), We show with a real and a synthetic experiment that our method can provide more accurate solutions than state-of-the-art methods when optimization is carried out in the vicinity of the global minimum of the contrast function. [less ▲]

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