Neural-based architectures for the segmentation of textures
Javier Ruíz-del-Solar, Daniel Kottow · 2002
An essential task in almost any pattern recognition system is the extraction of feature vectors, which are then used to perform a classification. Depending on the context of this classification, these feature vectors are expected to present invariance under basic transformations such as translation, scaling or rotation. Thus, every problem needs a careful selection of feature variables, which so far is mostly done by hand. Neural networks have been used successfully as classifiers for a long time, but only recently they have begun to be employed for automatic selection of feature variables. The ASSOM, ASGCS and ASGFC neural models are able to automatically select features variables (filters) for the segmentation of textures. In the paper three different texture segmentation architectures TEXSOM, TEXGFC and TEXSGFC, which are based on the mentioned neural models, are described.