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In this letter, we discuss the multivariate Laplace probability model in the context of a normal variance mixture model. We briefly review the derivation of the probability density function (pdf) and discuss a few important properties. We then present two methods for estimating its parameters from data and include an example of usage, where we apply the(More)
—In this paper, we introduce the homomorphic 0-WMAP (wavelet maximum a posteriori) filter, a wavelet-based statistical speckle filter equivalent to the well known 0-MAP filter. We perform a logarithmic transformation in order to make the speckle contribution additive and statistically independent of the radar cross section. Further, we propose to use the(More)
—This paper addresses estimation of the equivalent number of looks (ENL), an important parameter in statistical modelling of multilook synthetic aperture radar (SAR) images. Two new ENL estimators are discovered by looking at certain moments of the multilook polarimetric covariance matrix, which is commonly used to represent multilook polarimetric SAR data,(More)
—In this paper, we propose to use a matrix-variate Mellin transform in the statistical analysis of multilook polari-metric radar data. The domain of the transform integral is the cone of complex positive definite matrices, which allows for transformation of the distributions used to model the polarimetric covariance and coherency matrix. Based on the(More)
In this paper, a new statistical model for representing the amplitude statistics of ultrasonic images is presented. The model is called the Rician inverse Gaussian (RiIG) distribution, due to the fact that it is constructed as a mixture of the Rice distribution and the Inverse Gaussian distribution. The probability density function (pdf) of the RiIG model(More)
—This paper presents an automatic image segmen-tation method for Polarimetric SAR data. It utilises the full polarimetric information and incorporates texture by modelling with a non-Gaussian distribution for the complex scattering coefficients. The modelling is based upon the well known product model, with a Gamma distributed texture parameter, leading to(More)
In this paper, we discuss some equivalences between two recently introduced statistical learning schemes, namely Mercer kernel methods and information theoretic methods. We show that Parzen window-based estimators for some information theoretic cost functions are also cost functions in a corresponding Mercer kernel space. The Mercer kernel is directly(More)
We propose in this paper a new unsupervised neural network which is capable of clustering a set of experimental data according to a given generic interpoint similarity measure, and then assign to each new input its appropriate cluster label. The network is able to do this for clusters of any shape, and without knowing in advance the number of clusters to be(More)
—In this paper, we develop a Wiener-type speckle filter that operates in the stationary wavelet domain. We denote it as the stationary wavelet-domain Wiener (SWW) speckle filter. We assume that both the speckle-free image and the speckle contribution have spatial correlations and utilize well-established models for the power density spectrum of the radar(More)