Abstract:

This article describes the so-called TEXSOM-Architecture, a new Texture Segmentation Architecture based on the Joint Spatial/Spatial-Frequency paradigm. In this architecture the oriented filters are automatically generated using the Adaptive-Subspace Self Organizing Map (ASSOM) or the Supervised ASSOM (SASSOM) neural models. The automatic filter generation overcomes some drawbacks of similar architectures, such as the large size of the filter bank and the necessity of a priori knowledge to determine the filters' parameters. The quality of the segmentation process is improved by applying Median Filtering and the Watershed Transformation to the pre-segmented images.