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

Volume

1

fYear

2000

fDate

2000

Firstpage

9

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2000. Proceedings. 15th International Conference on

Conference_Location

Barcelona

ISSN

1051-4651

Print_ISBN

0-7695-0750-6

Type

conf

DOI

10.1109/ICPR.2000.905265

Filename

905265