2016
DOI: 10.1016/j.patrec.2015.10.006
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Rotation invariant texture descriptors based on Gaussian Markov random fields for classification

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Cited by 22 publications
(11 citation statements)
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“…In the method presented in (Almakady et al, 2018), the 2D-GMRF based method proposed in (Dharmagunawardhana et al, 2016) is extended to 3D-GMRF for volumetric texture classification. In this method, the 3D-GMRF model is generated at each voxel, where the estimated parameters of the model are employed as texture features in addition to the mean of a processed image region.…”
Section: Three-dimensional Gaussian Markov Random Fieldmentioning
confidence: 99%
See 1 more Smart Citation
“…In the method presented in (Almakady et al, 2018), the 2D-GMRF based method proposed in (Dharmagunawardhana et al, 2016) is extended to 3D-GMRF for volumetric texture classification. In this method, the 3D-GMRF model is generated at each voxel, where the estimated parameters of the model are employed as texture features in addition to the mean of a processed image region.…”
Section: Three-dimensional Gaussian Markov Random Fieldmentioning
confidence: 99%
“…Markov Random Fields (MRFs) have been introduced about two decades ago as a tool for solving visual perception problems (Wang et al, 2013). One important type of MRFs is the Gaussian Markov Random Fields (GMRF) which is among texture analysis methods that has been proved to demonstrate excellent performance in characterizing textures in 2D images, and are then employed for texture classification (Chellappa and Chatterjee, 1985;Dharmagunawardhana et al, 2016) and segmentation (Dharmagunawardhana et al, 2014;Manjunath and Chellappa, 1991;Mahmoodi and Gunn, 2011;Xia et al, 2006). GMRF model parameters can capture the essential structures of texture, and it has been a popular choice for modelling textures (Tuceryan and Jain, 1993).…”
Section: Introductionmentioning
confidence: 99%
“…Remote sensing analysis, medical image interpretation, pattern recognition and content-based image retrieval are some of the image-based applications, where texture analysis plays a fundamental and important role [1,2]. Texture representations can be classified into five categories in terms of the feature types employed, namely, statistical [3], structural [4], geometrical [5], model based [6] and signal processing features [7]. The majority of existing texture analysis methods make assumption that texture images are acquired from the same viewpoint (i.e.…”
Section: Introductionmentioning
confidence: 99%
“…Model-based methods function according to probability distributions in random fields in characterizing the texture images. The commonly used model-based methods are Autoregressive Model (AR) [6,7], Markov Chains and Markov Random Fields (MRFs) [8,9], Wold decomposition model [10] and spatial autocorrelation function model [11]. Many various filtering techniques are used for decomposition of texture images in the methods based on signal processing technique.…”
Section: Introductionmentioning
confidence: 99%