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G. Heidemann and D. Lucke and H. Ritter
ABSTRACT
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. Neural expert networks classify the features. It will be 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. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
A System for Various Visual Classification Tasks Based on Neural NetworksSite generated on Friday, 06 January 2006