The aim of this work is to perform a joint texture analysis in both discrete spaces. To achieve this goal, we propose a probabilistic vector texture model, ...
This work proposes a probabilistic vector texture model, using a Gauss-Markov random field (MRF), which allows the characterization of different ...
Texture feature analysis using a Gauss-Markov model in ...
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Oct 22, 2024 · The aim of this work is to perform a joint texture analysis in both discrete spaces. To achieve this goal, we propose a probabilistic vector ...
The MRF parameters allow the characterization of different hyperspectral textures. A possible application of this work is the classification of urban areas.
Texture feature analysis using a gauss-Markov model in hyperspectral image classification · Texture analysis through a markovian modelling and fuzzy ...
This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information.
Falzon and J. Zerubia, Texture feature analysis using a gauss-Markov model in hyperspectral image classi- fication, IEEE Transactions on Geoscience and Remote ...
Abstract: In this paper we deal with the problem of texture segmentation using a joint spectral and spatial analysis of pixel distribution.
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Rellier et al. [17] proposed a texture feature analysis using a Gauss-Markov random field model for HSI classification. In [18], [19], a ...
The MRF-model was used to synthesize various kinds of visible textures. Gaussian-Markov random fields (GMRF) were firstly used to sample the distribution of ...