Title :
A system for various visual classification tasks based on neural networks
Author :
Heidemann, Gunther ; Lücke, Dirk ; Ritter, Helge
Author_Institution :
AG Neuroinf., Bielefeld Univ., Germany
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. 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
Keywords :
adaptive systems; feature extraction; image classification; image segmentation; learning (artificial intelligence); neural nets; object recognition; principal component analysis; vector quantisation; PCA; adaptive systems; feature extraction; image classification; image segmentation; neural networks; object recognition; principal component analysis; vector quantization; Artificial neural networks; Computer architecture; Computer vision; Data mining; Feature extraction; Filters; Humans; Neural networks; Principal component analysis; Vector quantization;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.905265
