DocumentCode
3281312
Title
Shift invariant pattern recognition by associative memory
Author
Si, J. ; Michel, A.N.
Author_Institution
Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA
Volume
6
fYear
1992
fDate
10-13 May 1992
Firstpage
2913
Abstract
Presents a network architecture that can perform shift invariant pattern recognition. The network is composed of a preprocessing block and an associative memory block which is a recurrent neural network. The preprocessing network is designed in such a way that the output of this block is almost invariant under shifted input patterns. The associative memory block is employed to recall the original patterns stored in the system. A step by step design procedure for realizing shift invariant pattern recognition is provided
Keywords
content-addressable storage; image recognition; recurrent neural nets; associative memory; design procedure; network architecture; preprocessing block; recurrent neural network; shift invariant pattern recognition; Associative memory; Buildings; Data preprocessing; Equations; Humans; Neural networks; Neurons; Pattern recognition; Unsupervised learning; Visual system;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location
San Diego, CA
Print_ISBN
0-7803-0593-0
Type
conf
DOI
10.1109/ISCAS.1992.230641
Filename
230641
Link To Document