@article {690,
	title = {Gaussian MRF rotation-invariant features for image classification},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {26},
	year = {2004},
	pages = {951 - 955},
	abstract = {<p>Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate least squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features.</p>
},
	keywords = {Algorithms, anisotropic circular Gaussian MRF model, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, discrete Fourier transform, discrete Fourier transforms, Gaussian MRF rotation-invariant features, gray level cooccurrence probability features, image classification, image enhancement, image interpretation, image texture, Laplacian pyramid, Markov Chains, Markov processes, Markov Random Field models, Models, Normal Distribution, Pattern Recognition, Rotation, rotation-invariant texture features, Statistical, statistical improvement, texture rotation},
	issn = {0162-8828},
	doi = {http://dx.doi.org/10.1109/TPAMI.2004.30},
	author = {H Deng and D A. Clausi}
}
@inproceedings {694,
	title = {Advanced Gaussian MRF rotation-invariant texture features for classification of remote sensing imagery},
	booktitle = {2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2003},
	address = {Madison, WI, USA},
	abstract = {<p>The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. The paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modeling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.</p>
},
	keywords = {ACGMRF model, ALSE method, anisotropic circular Gaussian MRF, approximate least squares estimate, Brodatz imagery, discrete Fourier transform, discrete Fourier transforms, Feature Extraction, Gaussian distribution, image classification, image texture rotation, least squares approximation, least squares approximations, Markov processes, Markov random field, one-dimensional DFT, Remote Sensing, remote sensing image classification, rotated image texture modeling, rotation-invariant feature, rotation-invariant texture feature, SAR classification, sea ice, singularity problem, synthetic aperture radar},
	doi = {http://dx.doi.org/10.1109/CVPR.2003.1211533},
	author = {H Deng and D A. Clausi}
}
