A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparative study of AR order selection methods
Signal Processing
2-D high resolution spectral estimation based on multiple regions of support
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
IEEE Transactions on Signal Processing
Finite sample criteria for autoregressive order selection
IEEE Transactions on Signal Processing
Optimum quarter-plane autoregressive modeling of 2-D fields usingfour-field lattice approach
IEEE Transactions on Signal Processing
Two-dimensional orthogonal lattice structures for autoregressivemodeling of random fields
IEEE Transactions on Signal Processing
A two-dimensional fast lattice recursive least squares algorithm
IEEE Transactions on Signal Processing
Image compression in the wavelet domain using an AR texture model with compressed initial conditions
ISPRA'06 Proceedings of the 5th WSEAS International Conference on Signal Processing, Robotics and Automation
Least-modules estimates for spatial autoregression coefficients
Journal of Computer and Systems Sciences International
Dynamic texture analysis and synthesis using tensor decomposition
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Generalized M-estimates of the autoregression field coefficients
Automation and Remote Control
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In the context of parametric modeling for image processing, we derive an estimation method for both the order and the parameters of 2-D causal autoregressive model with different geometries of support. Model parameters are estimated from a lattice representation, i.e. based on reflection coefficients. Lattice parameter estimation algorithms offer advantages compared to the Yule-Walker method: they do not require matrix inversion and their computation are robust and fast. For order selection, information criterion (IC) methods are the most commonly used. Therefore our order selection method is based on the combination of an IC and the prediction errors of models computed from the lattice parameter estimation algorithm. In this paper, we favour two consistent criteria compared to the nonconsistent Akaike criterion: the first criterion is a 2-D extension of Bayesian information criterion; the second criterion, noted φβ, extended here to the 2-D case, is a generalization drawn on Rissanen's works. Simulations are provided on synthetic and natural textures with quarter plane support and non-symmetrical half plane support. We validate our results on natural textures using the Kullback divergence. The results show the interest of the combination of 2-DFLRLS algorithm and φβ, criterion to characterize natural textures.