Texture segmentation using moving average modeling approach

Supervised and unsupervised texture segmentation using features extracted with statistical modeling approach is considered. A neural network is used for supervised segmentation, and a fuzzy clustering algorithm is used for unsupervised segmentation. The model used in this approach is a two-dimensional moving average model, and parameters estimated by a maximum likelihood method are used as texture features. The performance of the segmentation algorithms using model features are demonstrated in the experiment with both synthetic and natural images.