A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture segmentation using a diffusion region growing technique
Pattern Recognition
A new algorithm for texture segmentation based on edge detection
Pattern Recognition
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IEEE Transactions on Information Theory
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent vocal cord image analysis for categorizing laryngeal diseases
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Image compression based on a family of stochastic models
Signal Processing
Increasing the discrimination power of the co-occurrence matrix-based features
Pattern Recognition
Towards a computer-aided diagnosis system for vocal cord diseases
Artificial Intelligence in Medicine
A neural approach to extract foreground from human movement images
Computer Methods and Programs in Biomedicine
Machine Vision and Applications
Texture segmentation with local fuzzy patterns and neuro-fuzzy decision support
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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In this paper, we use a two-dimensional (2-D) AR model for texture description. The coefficients of the AR model as the parameters can thus be used to identify textured images. These processes are ideally suited to implementation by neural networks which are well known for their parallel execution and adaptive learning abilities. The proposed network consists of three subnets, namely the input subnet (ISN), the analysis subnet (ASN) and the classification subnet (CSN), respectively. The neural network obtains parameters for a 2-D AR model on a given texture through an adaptive learning procedure, and segments an input image into regions with the learned textures. Furthermore, a textured image which has a certain degree of deformation with respect to one of the possible texture classes can be correctly classified by the network. The network is easy to extend because of its modular structure in which all channels work independently. A region growing technique for texture segmentation is implemented by comparing local region properties. It is able to grow all regions in a textured image simultaneously starting from initially decided internal regions until smooth boundaries are formed between all adjacent regions. The performance of the proposed network has been examined on real textured images. In the classification phase, images proceed through the network without the preprocessing and feature extraction required by many other techniques. Hence, overall computation time has been considerably reduced.