An assembly neural network based on the binary Hebbian rule is suggested for pattern recognition. The network consists of several sub-networks according to the number of classes to be recognized. Each sub-network consists of several neural columns according to the dimensionality of the signal space so that the value of each signal component is encoded by activity of adjacent neurons of the column. A new recognition algorithm is presented which realizes the nearest-neighbor method in the assembly neural network. Computer simulation of the network is performed. The model is tested on a texture segmentation task. The experiments have demonstrated that the network is able to segment reasonably real-world texture images.
A new heuristic algorithm is proposed for extraction of all homogeneous fine-grained texture segments present in any visual image. The segments extracted by this algorithm should comply with human understanding of homogeneous fine-grained areas. The algorithm sequentially extracts segments from more homogeneous to less homogeneous ones. The algorithm belongs to a region growing approach. So, for each segment, an initial seed point of this segment is found. Then, from this initial pixel, the segment begins to expand occupying its adjacent neighborhoods. This procedure of expansion of the segment continues till the segment reaches its borders. The algorithm examines neighboring pixels using texture features extracted in the image by means of a set of texture windows. The segmentation process terminates when the image contains no more sizable homogeneous segments. The segmentation procedure is fully unsupervised, i.e., it does not use a priori knowledge on either the type of textures or the number of texture segments in the image. Using black and white natural scenes, a series of experiments demonstrates efficiency of the algorithm in extraction of homogeneous fine-grained texture segments and the segmentation looks reasonable ''from a human point of view''.
Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture analysis such as segmentation plays a critical role in machine vision and pattern recognition applications. The widely applied areas are industrial automation, biomedical image processing and remote sensing. This paper describes a novel system for texture segmentation. We call this system Wavelet Oscillator Neural Networks (WONN). The proposed system is composed of two parts. A second-order statistical wavelet co-occurrence features are the first part of the proposed system and an oscillator neural network is in the second part of the system. The performance of the proposed system is tested on various texture mosaic images. The results of the proposed system are found to be satisfactory.