A system for various visual classification tasks based on neural networks
Gunther Heidemann, Dominik Lücke, Helge Joachim Ritter · 2002
A three stage recognition architecture that can be trained to different recognition or segmentation tasks is presented. It consists of an adaptive feature extraction based on vector quantization and local PCA. The features are classified by neural expert networks. It is shown that the system can be applied to object classification, segmentation of partially occluded objects and classification of object parts without modifications in the architecture.