Selective attention of high-order neural networks for invariant object recognition

Author

Zhou, Xiaozhong ; Koch, Mark W. ; Roberts, Morien W.

Author_Institution

Dept. of Electr. & Comput. Eng., Clarkson Univ., Potsdam, NY, USA

fYear

1991

fDate

8-14 Jul 1991

Abstract

Summary form only given. Selective attention can be used to reduce the number of inputs for a high-order neural network. By selecting an appropriate scanning mechanism, invariance to translation can be developed. Using a high-order neural network, rotation invariance can be achieved by encoding proper constraints on the connections of receptive fields. The authors have implemented a second-order recurrent neural network to recognize pixel based objects at any translation and 90° rotation and have tested the network with the TC problem

Keywords

computerised pattern recognition; computerised picture processing; invariance; neural nets; 90° rotation; TC problem; constraint encoding; high-order neural networks; invariant object recognition; receptive field connection; rotation invariance; scanning mechanism; second-order recurrent neural network; selective attention; translation invariance; Analog circuits; Coupling circuits; Feature extraction; Laboratories; Neural networks; Object recognition; Psychology; Spatial databases; Spatiotemporal phenomena; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7803-0164-1

Type

conf

DOI

10.1109/IJCNN.1991.155533

Filename

155533