منابع مشابه
Detecting fish in underwater video using the EM algorithm
We consider the problem of detecting fish in underwater video. We adopt a modeling framework, where the shape of each fish is assumed to be multivariate Gaussian. Mixture modeling is used to classify noise and varying numbers of fish. The mixture parameters are estimated using an EM algorithm that incorporates an Akaike information criterion to simultaneously estimate the number of components i...
متن کاملUsing the EM algorithm to weight data sets of unknown precision when modelling fish stocks.
Stocks of commercial fish are often modelled using sampling data of various types, of unknown precision, and from various sources assumed independent. We want each set to contribute to estimates of the parameters in relation to its precision and goodness of fit with the model. Iterative re-weighting of the sets is proposed for linear models until the weight of each set is found to be proportion...
متن کاملEstimation of the Parameters of the Lomax Distribution using the EM Algorithm and Lindley Approximation
Estimation of statistical distribution parameter is one of the important subject of statistical inference. Due to the applications of Lomax distribution in business, economy, statistical science, queue theory, internet traffic modeling and so on, in this paper, the parameters of Lomax distribution under type II censored samples using maximum likelihood and Bayesian methods are estimated. Wherea...
متن کاملCorrections to "Adaptive Snakes Using the EM Algorithm"
There is a typographical error in a sentence following (20) in [1, p.1681], in which an extra parenthesis appears. The corrected sentence is as follows. Therefore, p(v) is a Gibbs distribution p(v) = 1=Vintexp E . Also, the authors’ photographs and biographies did not appear in [1], and they now appear below. In addition, some of the information in references [9], [24], and [28] has changed. Th...
متن کاملLearning to Classify Galaxy Shapes Using the EM Algorithm
We describe the application of probabilistic model-based learning to the problem of automatically identifying classes of galaxies, based on both morphological and pixel intensity characteristics. The EM algorithm can be used to learn how to spatially orient a set of galaxies so that they are geometrically aligned. We augment this “ordering-model” with a mixture model on objects, and demonstrate...
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ژورنال
عنوان ژورنال: ICES Journal of Marine Science
سال: 2009
ISSN: 1095-9289,1054-3139
DOI: 10.1093/icesjms/fsp027