A simulation-based estimator for hidden Markov random fields

Veijanen, Ari

doi:10.1109/34.85674

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…The segmentation methods we presented in this paper are local and it is well known, in the hard case, that global hidden Markov model-based methods [1], [7], [8], [11], [12], [14], [17], [18], [20], [21], [25], [29], [31] are much more efficient in several situations. However, it has been established [3] that local methods can be competitive in some situations and we conjecture, as the hard framework can be seen as a particular case of the fuzzy one, that the same is true in the fuzzy context.…”

Section: Discussionmentioning

confidence: 99%

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Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation

Caillol¹,

Pieczynski²,

Hillion

1997

IEEE Trans. on Image Process.

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This paper addresses the estimation of fuzzy Gaussian distribution mixture with applications to unsupervised statistical fuzzy image segmentation. In a general way, the fuzzy approach enriches the current statistical models by adding a fuzzy class, which has several interpretations in signal processing. One such interpretation in image segmentation is the simultaneous appearance of several thematic classes on the same site. We introduce a new procedure for estimating of fuzzy mixtures, which is an adaptation of the iterative conditional estimation (ICE) algorithm to the fuzzy framework, We first describe the blind estimation, i.e., without taking into account any spatial information, valid in any context of independent noisy observations. Then we introduce, in a manner analogous to classical hard segmentation, the spatial information by two different approaches: contextual segmentation and adaptive blind segmentation. In the first case, the spatial information is taken into account at the segmentation step level, and in the second case it is taken into account at the parameter estimation step level. The results obtained with the iterative conditional estimation algorithm are compared to those obtained with expectation-maximization (EM) and the stochastic EM algorithms, on both parameter estimation and unsupervised segmentation levels, via simulations. The methods proposed appear as complementary to the fuzzy C-means algorithms.

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Section: Discussionmentioning

confidence: 99%

“…There are two main approaches for statistical image segmentation: the global approach [1], [3], [7], [11], [12], [14], [17], [18], [20], [21], [25], [26], [28], [29], [31], and the local one [3], [4], [22], and [24]. A global method takes into account the values of in the entire image.…”

Section: A Segmentation Rulementioning

confidence: 99%