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
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;
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
DOI :
10.1109/ISCAS.1992.230641
