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
Finite sample criteria for autoregressive order selection
IEEE Transactions on Signal Processing
A unified texture model based on a 2-D Wold-like decomposition
IEEE Transactions on Signal Processing
Double Markov random fields and Bayesian image segmentation
IEEE Transactions on Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster validation for unsupervised stochastic model-based image segmentation
IEEE Transactions on Image Processing
Segmentation of textured images using a multiresolution Gaussian autoregressive model
IEEE Transactions on Image Processing
Evaluation for uncertain image classification and segmentation
Pattern Recognition
Computer Vision and Image Understanding
Review article: Automated fabric defect detection-A review
Image and Vision Computing
Computer Vision and Image Understanding
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In the context of the model-based methods for image processing, we propose some improvements for an unsupervised textured image segmentation algorithm using a 2-D quarter plane autoregressive model. The segmentation algorithm works in two stages: The first stage consists in an estimation of both the number of textures and the model parameters associated with each existing texture. The estimation is achieved by minimizing a probabilistic criterion which comprises a penalty term such as those used in information criteria (IC). The second stage deals with a maximum a posteriori estimation of the label field by a simulated annealing method. In a former work, Akaike IC (AIC) and a 2-D first quarter plane autoregressive model with fixed (1,1) order were used. In order to estimate the number of textures and the model orders, we propose to use Bayesian IC (BIC) and @f"@bIC. Moreover, during the two stages of the algorithm, the four quarter planes prediction supports have been used in order to solve problems at image and region boundaries. The results are given on images containing synthetic and natural textures.