Learn More
We conjecture that texture can be characterized by the statistics of the wavelet detail coefficients and therefore introduce two feature sets: (1) the wavelet histogram signatures which capture all first order statistics using a model based approach and (2) the wavelet co-occurrence signatures, which reflect the coefficients' second-order statistics. The(More)
The problem of parameter estimation from Rician distributed data (e.g., magnitude magnetic resonance images) is addressed. The properties of conventional estimation methods are discussed and compared to maximum-likelihood (ML) estimation which is known to yield optimal results asymptotically. In contrast to previously proposed methods, ML estimation is(More)
This paper describes a novel data clustering algorithm, which is a hybrid approach combining a genetic algorithm with the classical c-means clustering algorithm (CMA). The proposed technique is superior to CMA in the sense that it converges to a nearby global optimum rather than a local one. As an application the problem of color image quantization is(More)
This contribution concerns the retrieval of colour texture. The interband correlation structure is considered by modeling the heavy-tailed image wavelet histograms with a multivariate generalized Gaussian. As a similarity measure we propose to use the Rao geodesic distance, which, in contrast to the Kullback-Leibler divergence, exists in a closed form for(More)
In the last decade, multiscale techniques for gray-level texture analysis have been intensively used. In this paper, we aim on extending these techniques to color images. We i n troduce wavelet energy-correlation signatures and we derive the transformation of these signatures upon linear color space transformations. Experiments are conducted on a set of 30(More)
—Spectral mixtures observed in hyperspectral imagery often display non-linear mixing effects. Since most traditional unmixing techniques are based upon the linear mixing model, they perform poorly in finding the correct endmembers and their abundances in the case of non-linear spectral mixing. In this paper, we present an unmixing algorithm that is capable(More)
This paper exploits the interband correlations of color and multispectral images for wavelet-based denoising. For this, a multispectral extension of the linear minimum mean squared error estimation (LMMSE) is constructed to estimate the signal from the observed wavelet coefficients. The calculation involves the signal autocovariance matrices , which are(More)