Jirí Grim

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The paper deals with estimation of finite distribution mixtures which are practically important in cluster analysis, pattern recognition and other fields. After a brief survey of existing methods attention is confined to maximum-likelihood estimates, especially to an iterative procedure frequently discussed in the recent literature. It is shown that this(More)
We propose a new approach to diagnostic evaluation of screening mammograms based on local statistical texture models. The local evaluation tool has the form of a multivariate probability density of gray levels in a suitably chosen search window. First, the density function in the form of Gaussian mixture is estimated from data obtained by scanning of the(More)
Abstrakt It is well known that log-likelihood function for finite mixtures usually has local maxima and therefore the iterative EM algorithm for maximum-likelihood estimation of mixtures may be starting-point dependent. In the present paper we propose a method of choosing initial parameters of mixtures which includes two stages: (a) computation of(More)
The main motivation of the present paper is to design a statistically well justified and biologically compatible neural network model and, in particular, to suggest a theoretical interpretation of the well known high parallelism of biological neural networks. We consider a novel probabilistic approach to neural networks developed in the framework of(More)
A new method of texture modelling based on discrete distribution mixtures is proposed. Unlike some alternative approaches the statistical properties of textures are modelled by a discrete distribution mixture of product components. The univariate distributions in the products are represented in full generality by vectors of probabilities without any(More)
Assuming local and shift-invariant texture properties we describe the statistical dependencies between pixels by a joint probability density of gray-levels within a suitably chosen observation window. We estimate the unknown multi-variate density in the form of a Gaussian mixture of product components from data obtained by shifting the observation window.(More)
A new method of colour texture modelling based on Gaussian distribution mixtures is discussed. We estimate the local statistical properties of the monospectral version of the target texture in the form of a Gaussian mixture of product components. The synthesized texture is obtained by means of a step-wise prediction of the texture image. In order to achieve(More)