TEXTURED IMAGE SEGMENTATION USING AUTOREGRESSIVE MODEL AND ARTIFICIALNEURAL-NETWORK

In this paper, we use a two-dimensional (2-D) AR model for texture des cription. The coefficients of the AR model as the parameters can thus be used to identify textured images. These processes are ideally suite d to implementation by neural networks which are well known for their parallel execution and adaptive learning abilities. The proposed netwo rk consists of three subnets, namely the input subnet (ISN), the analy sis subnet (ASN) and the classification subnet (CSN), respectively. Th e neural network obtains parameters for a 2-D AR model on a given text ure through an adaptive learning procedure, and segments an input imag e into regions with the learned textures. Furthermore, a textured imag e 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 whic h all channels work independently. A region growing technique for text ure segmentation is implemented by comparing local region properties. It is able to grow all regions in a textured image simultaneously star ting from initially decided internal regions until smooth boundaries a re formed between all adjacent regions. The performance of the propose d network has been examined on real textured images. In the classifica tion phase, images proceed through the network without the preprocessi ng and feature extraction required by many other techniques. Hence, ov erall computation time has been considerably reduced.